{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Datasets can be downloaded from https://www.dropbox.com/sh/dfqht1ob89ku99d/AACI5ZW3aRuq9MhBfSNS_1O_a?dl=0**  \n",
    "Ref: Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20-31 (2016)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import stream as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.3.9'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "with open('/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/analysis/stream_result/stream_result.pkl', 'rb') as f:\n",
    "    data = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using default working directory.\n",
      "Saving results in: /Users/huidong/Projects/Github/STREAM/tutorial/stream_result\n"
     ]
    }
   ],
   "source": [
    "adata=st.read(file_name='./data_Nestorowa.tsv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/data/Chow1/Chow_1_Test/outs/stream/genes.tsv\n",
      "/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/data/Chow1/Chow_1_Test/outs/stream/barcodes.tsv\n",
      "Using default working directory.\n",
      "Saving results in: /net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/analysis/stream_result\n"
     ]
    }
   ],
   "source": [
    "adata=st.read(file_name='/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/data/Chow1/Chow_1_Test/outs/stream/matrix.mtx',file_format='mtx')\n",
    "adata.var_names_make_unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load and use 10x Genomics single cell RNA-seq data processed with Cell Ranger:  \n",
    "(Make sure **'genes.tsv'** and **'barcodes.tsv'** are under the same folder as **'matrix.mtx'**)  \n",
    "\n",
    "```python\n",
    "adata=st.read(file_name='./filtered_gene_bc_matrices/hg19/matrix.mtx',file_format='mtx')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No cell label file is provided, 'unknown' is used as the default cell labels\n",
      "No cell color file is provided, random color is generated for each cell label\n"
     ]
    }
   ],
   "source": [
    "st.add_cell_labels(adata)\n",
    "st.add_cell_colors(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### read in cell labels and label color\n",
    "\n",
    "if cell label file or cell color file is not provided, please simply run:\n",
    "\n",
    "'unknown' will be added as the default label for all cells  \n",
    "`st.add_cell_labels(adata)`\n",
    "\n",
    "'random color will be generated for each cell label  \n",
    "`st.add_cell_colors(adata)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "st.add_cell_labels(adata,file_name='./cell_label.tsv.gz')\n",
    "st.add_cell_colors(adata,file_name='./cell_label_color.tsv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "remove mitochondrial genes:\n",
      "['mt-Nd1', 'mt-Nd2', 'mt-Co1', 'mt-Co2', 'mt-Atp8', 'mt-Atp6', 'mt-Co3', 'mt-Nd3', 'mt-Nd4l', 'mt-Nd4', 'mt-Nd5', 'mt-Nd6', 'mt-Cytb']\n",
      "No filtering\n"
     ]
    }
   ],
   "source": [
    "st.normalize_per_cell(adata)\n",
    "st.log_transform(adata)\n",
    "st.remove_mt_genes(adata)\n",
    "st.filter_cells(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### other useful preprocessing steps when dealing with raw-count data\n",
    "\n",
    "Normalize gene expression based on library size  \n",
    "`st.normalize_per_cell(adata)`\n",
    "\n",
    "Logarithmize gene expression  \n",
    "`st.log_transform(adata)` \n",
    "\n",
    "Remove mitochondrial genes  \n",
    "`st.remove_mt_genes(adata)`\n",
    "\n",
    "Filter out cells  \n",
    "`st.filter_cells(adata)` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter out genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter genes based on min_num_cells\n",
      "After filtering out low-expressed genes: \n",
      "4803 cells, 15718 genes\n"
     ]
    }
   ],
   "source": [
    "st.filter_genes(adata,min_num_cells = max(5,int(round(adata.shape[0]*0.001))),\n",
    "                min_pct_cells = None,expr_cutoff = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "st.select_variable_genes?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check parameters**  \n",
    "`st.select_variable_genes?`\n",
    "\n",
    "Please check if the blue curve fits the points well.  \n",
    "If not, try to lower the parameter **'loess_frac'** (By default, `loess_frac=0.1`) until the blue curve fits well.   \n",
    "\n",
    "e.g. `st.select_variable_genes(adata,loess_frac=0.01)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "786 variable genes are selected\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.select_variable_genes(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "alternatively, user can also select top  principal components\n",
    "\n",
    "`st.select_top_principal_components?`  \n",
    "`st.select_top_principal_components(adata)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using all the genes ...\n",
      "15 PCs are selected\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.select_top_principal_components(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "st.dimension_reduction?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check parameters**  \n",
    "`st.dimension_reduction?`\n",
    "\n",
    "Tips:\n",
    "\n",
    ">by default `n_components =3`  \n",
    "For biological process with simple bifurcation or linear trajectory, two components would be recommended  \n",
    "e.g, `st.dimension_reduction(adata,n_components =2)`\n",
    "\n",
    ">Several alternative dimension reduction methods are also supported, `se`(spectral embedding), `umap`, `pca`.  \n",
    "by default, `method ='mlle'`.  \n",
    "- For **large dataset**, `se`(Spectral Embedding) works faster than MLLE while preserving the similar compact structure to **MLLE**.  \n",
    "e.g. `st.dimension_reduction(adata,method ='se')`\n",
    "- For **large dataset**, lowering the percentage of neighbors (`nb_pct=0.1` by default) will speed up this step  \n",
    "e.g, `st.dimension_reduction(adata,nb_pct =0.01)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature var_genes is being used ...\n",
      "16 cpus are being used ...\n"
     ]
    }
   ],
   "source": [
    "st.dimension_reduction(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot_dimension_reduction(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check parameters**  \n",
    "`st.plot_visualization_2D?`\n",
    "\n",
    "Tips:  \n",
    "> Before the downstream **elastic principal graph learning**, it is important to visualize the top components in 2D plane with **UMAP** (by default) or **tSNE**(`st.plot_visualization_2D(adata,method='tsne')`) to confirm the existence of meaningful biological trajectory pattern "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/numba/compiler.py:602: NumbaPerformanceWarning: \n",
      "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n",
      "\n",
      "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n",
      "\n",
      "File \"../../../../../../../../restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/umap/rp_tree.py\", line 135:\n",
      "@numba.njit(fastmath=True, nogil=True, parallel=True)\n",
      "def euclidean_random_projection_split(data, indices, rng_state):\n",
      "^\n",
      "\n",
      "  self.func_ir.loc))\n",
      "/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/umap/nndescent.py:92: NumbaPerformanceWarning: \n",
      "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n",
      "\n",
      "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n",
      "\n",
      "File \"../../../../../../../../restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/umap/utils.py\", line 409:\n",
      "@numba.njit(parallel=True)\n",
      "def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state):\n",
      "^\n",
      "\n",
      "  current_graph, n_vertices, n_neighbors, max_candidates, rng_state\n",
      "/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/numba/compiler.py:602: NumbaPerformanceWarning: \n",
      "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n",
      "\n",
      "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n",
      "\n",
      "File \"../../../../../../../../restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/umap/nndescent.py\", line 47:\n",
      "    @numba.njit(parallel=True)\n",
      "    def nn_descent(\n",
      "    ^\n",
      "\n",
      "  self.func_ir.loc))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot_visualization_2D(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check parameters**  \n",
    "`st.seed_elastic_principal_graph?`\n",
    "\n",
    "Tips:\n",
    ">To better scale up STREAM to large datasets, since **version 0.3.8**, the default **clustering method** has been changed from **'ap'** (affinity propagation) to **'kmeans'**. Users can specify `clustering = 'ap'` to reproduce the analyses in STREAM paper:  \n",
    "i.e. `st.seed_elastic_principal_graph(adata,clustering='ap')`\n",
    "\n",
    ">If cells form a big bulk in MLLE space, **'ap'** may generate too many branches.   \n",
    "In that case, try `clustering = 'kmeans'` or `clustering = 'sc'` to avoid a too complex initial strcuture\n",
    "\n",
    ">For noisy dataset, **spectral clustering** is proved to work better to get rid of noisy branches  \n",
    "e.g. `st.seed_elastic_principal_graph(adata,clustering='sc',n_clusters=10)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seeding initial elastic principal graph...\n",
      "Clustering...\n",
      "K-Means clustering ...\n",
      "The number of initial nodes is 10\n",
      "Calculatng minimum spanning tree...\n",
      "Number of initial branches: 5\n"
     ]
    }
   ],
   "source": [
    "st.seed_elastic_principal_graph(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot_branches(adata)\n",
    "st.plot_branches_with_cells(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check parameters**  \n",
    "`st.elastic_principal_graph?`\n",
    "\n",
    "Tips:  \n",
    "- Increase the parameter **'epg_alpha'** will help control spurious branches(by default `epg_alpha=0.02`)  \n",
    "e.g. `st.elastic_principal_graph(adata,epg_alpha=0.03)`  \n",
    "\n",
    "\n",
    "- Add **'epg_trimmingradius'** will help get rid of noisy points (by defalut `epg_trimmingradius=Inf`)   \n",
    "e.g. `st.elastic_principal_graph(adata,epg_trimmingradius=0.1)`  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning elastic principal graph...\n",
      "[1]\n",
      " \"Constructing tree 1 of 1 / Subset 1 of 1\"\n",
      "\n",
      "\n",
      "[1]\n",
      " \"Computing EPG with 50 nodes on 4803 points and 3 dimensions\"\n",
      "\n",
      "\n",
      "[1]\n",
      " \"Using a single core\"\n",
      "\n",
      "\n",
      "Nodes = \n",
      "10\n",
      " \n",
      "11\n",
      " \n",
      "12\n",
      " \n",
      "13\n",
      " \n",
      "14\n",
      " \n",
      "15\n",
      " \n",
      "16\n",
      " \n",
      "17\n",
      " \n",
      "18\n",
      " \n",
      "19\n",
      " \n",
      "20\n",
      " \n",
      "21\n",
      " \n",
      "22\n",
      " \n",
      "23\n",
      " \n",
      "24\n",
      " \n",
      "25\n",
      " \n",
      "26\n",
      " \n",
      "27\n",
      " \n",
      "28\n",
      " \n",
      "29\n",
      " \n",
      "30\n",
      " \n",
      "31\n",
      " \n",
      "32\n",
      " \n",
      "33\n",
      " \n",
      "34\n",
      " \n",
      "35\n",
      " \n",
      "36\n",
      " \n",
      "37\n",
      " \n",
      "38\n",
      " \n",
      "39\n",
      " \n",
      "40\n",
      " \n",
      "41\n",
      " \n",
      "42\n",
      " \n",
      "43\n",
      " \n",
      "44\n",
      " \n",
      "45\n",
      " \n",
      "46\n",
      " \n",
      "47\n",
      " \n",
      "48\n",
      " \n",
      "49\n",
      " \n",
      "\n",
      "\n",
      "BARCODE\tENERGY\tNNODES\tNEDGES\tNRIBS\tNSTARS\tNRAYS\tNRAYS2\tMSE\tMSEP\tFVE\tFVEP\tUE\tUR\tURN\tURN2\tURSD\n",
      "\n",
      "3||50\n",
      "\t\n",
      "2.755e-05\n",
      "\t\n",
      "50\n",
      "\t\n",
      "49\n",
      "\t\n",
      "42\n",
      "\t\n",
      "3\n",
      "\t\n",
      "0\n",
      "\t\n",
      "0\n",
      "\t\n",
      "1.12e-05\n",
      "\t\n",
      "1.068e-05\n",
      "\t\n",
      "0.9821\n",
      "\t\n",
      "0.9829\n",
      "\t\n",
      "1.519e-05\n",
      "\t\n",
      "1.15e-06\n",
      "\t\n",
      "5.748e-05\n",
      "\t\n",
      "0.002874\n",
      "\t\n",
      "0\n",
      "\n",
      "\n",
      "23.088 sec elapsed\n",
      "\n",
      "[[1]]\n",
      "\n",
      "\n",
      "\n",
      "Number of branches after learning elastic principal graph: 7\n"
     ]
    }
   ],
   "source": [
    "st.elastic_principal_graph(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot_branches(adata)\n",
    "st.plot_branches_with_cells(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tips:  \n",
    "- Add **'epg_trimmingradius'** will help get rid of noisy points (by defalut `epg_trimmingradius=Inf`)   \n",
    "e.g. `st.optimize_branching(adata,epg_trimmingradius=0.1)`  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizing branching...\n",
      "[1]\n",
      " \"Constructing tree 1 of 1 / Subset 1 of 1\"\n",
      "\n",
      "\n",
      "[1]\n",
      " \"Computing EPG with 80 nodes on 4803 points and 3 dimensions\"\n",
      "\n",
      "\n",
      "[1]\n",
      " \"Using a single core\"\n",
      "\n",
      "\n",
      "Nodes = \n",
      "50\n",
      " \n",
      "51\n",
      " \n",
      "52\n",
      " \n",
      "53\n",
      " \n",
      "54\n",
      " \n",
      "55\n",
      " \n",
      "56\n",
      " \n",
      "57\n",
      " \n",
      "58\n",
      " \n",
      "59\n",
      " \n",
      "60\n",
      " \n",
      "61\n",
      " \n",
      "62\n",
      " \n",
      "63\n",
      " \n",
      "64\n",
      " \n",
      "65\n",
      " \n",
      "66\n",
      " \n",
      "67\n",
      " \n",
      "68\n",
      " \n",
      "69\n",
      " \n",
      "70\n",
      " \n",
      "71\n",
      " \n",
      "72\n",
      " \n",
      "73\n",
      " \n",
      "74\n",
      " \n",
      "75\n",
      " \n",
      "76\n",
      " \n",
      "77\n",
      " \n",
      "78\n",
      " \n",
      "79\n",
      " \n",
      "\n",
      "\n",
      "BARCODE\tENERGY\tNNODES\tNEDGES\tNRIBS\tNSTARS\tNRAYS\tNRAYS2\tMSE\tMSEP\tFVE\tFVEP\tUE\tUR\tURN\tURN2\tURSD\n",
      "\n",
      "3||80\n",
      "\t\n",
      "1.475e-05\n",
      "\t\n",
      "80\n",
      "\t\n",
      "79\n",
      "\t\n",
      "72\n",
      "\t\n",
      "3\n",
      "\t\n",
      "0\n",
      "\t\n",
      "0\n",
      "\t\n",
      "7.675e-06\n",
      "\t\n",
      "7.424e-06\n",
      "\t\n",
      "0.9877\n",
      "\t\n",
      "0.9881\n",
      "\t\n",
      "6.161e-06\n",
      "\t\n",
      "9.12e-07\n",
      "\t\n",
      "7.296e-05\n",
      "\t\n",
      "0.005837\n",
      "\t\n",
      "0\n",
      "\n",
      "\n",
      "6.507 sec elapsed\n",
      "\n",
      "Number of branches after optimizing branching: 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### optional step\n",
    "st.optimize_branching(adata)\n",
    "st.plot_branches(adata)\n",
    "st.plot_branches_with_cells(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other optional steps:\n",
    "\n",
    "- Prune branches:  \n",
    "`st.prune_elastic_principal_graph?`  \n",
    "`st.prune_elastic_principal_graph(adata)`\n",
    "\n",
    "\n",
    "- Shift branching node:  \n",
    "`st.shift_branching?`  \n",
    "`st.shift_branching(adata)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check parameters**  \n",
    "`st.extend_elastic_principal_graph?`  \n",
    "\n",
    "Tips:  \n",
    "- Add **'epg_trimmingradius'** will help get rid of noisy points (by defalut `epg_trimmingradius=Inf`)   \n",
    "e.g. `st.extend_elastic_principal_graph(adata,epg_trimmingradius=0.1)`  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extending leaves with additional nodes ...\n",
      "Number of branches after extending leaves: 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Extend leaf branch to reach further cells \n",
    "st.extend_elastic_principal_graph(adata)\n",
    "st.plot_branches(adata)\n",
    "st.plot_branches_with_cells(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot_flat_tree(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Validate the learned structure by visualizing the branch assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing precomputed umap visualization ...\n",
      "Importing precomputed umap visualization ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot_visualization_2D(adata)\n",
    "st.plot_visualization_2D(adata,color_by='branch',fig_legend_ncol=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check parameters**  \n",
    "`st.subwaymap_plot?`\n",
    "\n",
    "By default **percentile_dist=95**, to make cells more conpact around the branches, try to **increase percentile_dist to beween 95 and 100**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.subwaymap_plot(adata,root='S4',fig_legend_ncol=6) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.subwaymap_plot(adata,root='S4',fig_legend_ncol=6,percentile_dist=100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check parameters**  \n",
    "`st.stream_plot?`\n",
    "\n",
    "By default **factor_min_win=2.0**, lowering it (between 1.0 and 2.0) can make smoother stream plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.stream_plot(adata,root='S4',fig_legend_ncol=6,fig_size=(8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.stream_plot(adata,root='S4',fig_legend_ncol=6,fig_size=(8,8),factor_min_win=1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The order between **horizontal branches from the same parent node** has no meaning\n",
    "\n",
    "Users can specify the order preference of nodes themselves by setting the parameter **'preference'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.subwaymap_plot(adata,percentile_dist=100,root='S4',fig_legend_ncol=6,preference=['S5']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.subwaymap_plot(adata,percentile_dist=100,root='S4',fig_legend_ncol=6,preference=['S2','S1']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.stream_plot(adata,root='S4',fig_legend_ncol=6,fig_size=(8,8),factor_min_win=1.2,preference=['S2','S1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For stream plot with **'thin branches'**, log2 view of stream plot (by specifying **'flag_log_view=True'**) will help zoom in the thin branches with very few cells by log2 transformation and visualize the composition of cells on the thin branches  \n",
    "**factor_zoomin**: suggested value 50~200\n",
    "\n",
    "**This case is best illustrated in tutorial** [4.STREAM_scATAC-seq](https://nbviewer.jupyter.org/github/pinellolab/STREAM/blob/master/tutorial/4.STREAM_scATAC-seq.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./img/log2_view_plots.png\" alt=\"log2_view_plots\" width=1000>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.subwaymap_plot_gene(adata,percentile_dist=100,root='S4',preference=['S2','S1'],genes=['Pecam1', 'Nrp1', 'Kdr','Oit3']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.stream_plot_gene(adata,root='S4',fig_size=(8,8),factor_min_win=1.2,preference=['S2','S1'],genes=['Pecam1', 'Nrp1', 'Kdr','Oit3','Cyp2e1','Alb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Marker gene detection part is a bit time-consuming, so please make sure the struture learned from previous steps is reasonble before running any maker gene detection steps**\n",
    "\n",
    "**Also it's not always necessary to execute all three marker gene detection parts. Running one of them might be adequate already.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.Detect marker genes for each leaf branch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**'preference'** is only used to adjust the order of comparions (in coordance with subway_map plot and stream plot), it will not affect the final detected genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum number of cells expressing genes: 5\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-e97cbeffdffd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetect_leaf_genes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'S4'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpreference\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'S2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'S1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/stream/core.py\u001b[0m in \u001b[0;36mdetect_leaf_genes\u001b[0;34m(adata, cutoff_zscore, cutoff_pvalue, percentile_expr, n_jobs, use_precomputed, root, preference)\u001b[0m\n\u001b[1;32m   4463\u001b[0m                 \u001b[0mkurskal_statistic\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkurskal_pvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkruskal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlist_br_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4464\u001b[0m                 \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkurskal_pvalue\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mcutoff_pvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4465\u001b[0;31m                     df_conover_pvalues= posthoc_conover(df_gene_detection[[x in leaf_edges for x in df_gene_detection['bfs_edges']]], \n\u001b[0m\u001b[1;32m   4466\u001b[0m                                                        val_col=gene, group_col='bfs_edges', p_adjust = 'fdr_bh')\n\u001b[1;32m   4467\u001b[0m                     \u001b[0mcand_conover_pvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_conover_pvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mdf_conover_pvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcand_br\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcand_br\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2969\u001b[0m         \u001b[0;31m# Do we have a (boolean) 1d indexer?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2970\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2971\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_bool_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2973\u001b[0m         \u001b[0;31m# We are left with two options: a single key, and a collection of keys,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_bool_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3023\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3024\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3025\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3027\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indices, axis, is_copy, **kwargs)\u001b[0m\n\u001b[1;32m   3602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3603\u001b[0m         new_data = self._data.take(\n\u001b[0;32m-> 3604\u001b[0;31m             \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3605\u001b[0m         )\n\u001b[1;32m   3606\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[1;32m   1395\u001b[0m         \u001b[0mnew_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1396\u001b[0m         return self.reindex_indexer(\n\u001b[0;32m-> 1397\u001b[0;31m             \u001b[0mnew_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1398\u001b[0m         )\n\u001b[1;32m   1399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[1;32m   1265\u001b[0m                     ),\n\u001b[1;32m   1266\u001b[0m                 )\n\u001b[0;32m-> 1267\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1268\u001b[0m             ]\n\u001b[1;32m   1269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1265\u001b[0m                     ),\n\u001b[1;32m   1266\u001b[0m                 )\n\u001b[0;32m-> 1267\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1268\u001b[0m             ]\n\u001b[1;32m   1269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mtake_nd\u001b[0;34m(self, indexer, axis, new_mgr_locs, fill_tuple)\u001b[0m\n\u001b[1;32m   1312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m         new_values = algos.take_nd(\n\u001b[0;32m-> 1314\u001b[0;31m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_fill\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_fill\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m         )\n\u001b[1;32m   1316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36mtake_nd\u001b[0;34m(arr, indexer, axis, out, fill_value, mask_info, allow_fill)\u001b[0m\n\u001b[1;32m   1719\u001b[0m         \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1720\u001b[0m     )\n\u001b[0;32m-> 1721\u001b[0;31m     \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1723\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mflip_order\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "st.detect_leaf_genes(adata,root='S4',preference=['S2','S1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'leaf_genes_all'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-00dbf263239d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0madata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'leaf_genes_all'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'leaf_genes_all'"
     ]
    }
   ],
   "source": [
    "adata.uns['leaf_genes_all']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.Detect transition gene for each branch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum number of cells expressing genes: 5\n",
      "Importing precomputed scaled gene expression matrix ...\n"
     ]
    }
   ],
   "source": [
    "st.detect_transistion_genes(adata,root='S4',preference=['S2','S1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Bft1TQl0/yzFXaL2srgc/maaJ0zSJDPIg+7CTYF8L/t4WPK0GUcEe/Lqn9JT62p7loEWYa0W5VQNPVu0oAeDyuh7kFbkebppb6KSOn75KRKpb+/btycvLY9OmTZSVlTF79mzuu+8+d/3999/PlClTyM/PZ8OGDXTt2tVdl5GRQXZ2Njt37uTdd991l2VmZrJnzx6mT5/OQw89xO+//37O53Wh0aeZiEgN8vfvC3i8o/9x69/8Pp+Obx0gt9AkzG4l2NdC9mEne/PKyC928tOuEnILXSvG6/eW0jspi95JWTw2L6dSW81CPFiWWoxpmizZWkz2Ydd1naK8+eDnw3T71wE++Pkw/Vr7Vs9kRaSCI6vG3377LU2bNqVBgwbuOk9PT3777Tfy8vIIDAykdevW7jqLxcLo0aPx9vbGZrO5y8eOHYu3tzcxMTF0796dOXPmnNP5XIgUGIuI1BCLtxTRMsyTIN/jf3Q/2TmA5Y/XpWFtK3NSDmMYBq/2sPPEp7k8Ni+XpiEehAa4rj86leLtOwMBGDgrm95JWXzxWyFd/+LDX+p40Ht6Nj9sL6FJiOu2lPGL83i2qz/JQ+vy15gAJiTnV//kRYQBAwYwa9YskpKSKqRRAMydO5eFCxcSERFBTEwMK1eudNfVrVsXHx+fCucHBgbi5+fnPo6IiCA9XXsp6+Y7EZEaYmNGKSt3lNAvLZvN+0vZlungnbtqE17b9VFe5DDx8TAwDINaPgY2T1dqRfsIbz4e5E1BsZMhs3O4uoEX+wvKjtnHjH5BFY6f6RIAwNLUYjzKszdMcAfndfws5JbnLItI9YqIiKBRo0YsXLiQ999/v0JdmzZt+OyzzygtLeWtt97i7rvvJi0tDQDDMCq1lZOTw6FDh9zB8a5du7jyyiurfxIXOAXGIiI1xJOdA3iys+t1wvxc+rb2ZWumg1U7S7irpS+jv85jywEHThMig6wMv8H1k+nYb/NYm16K1QIvdAvA28P1JXkkleKIGf0C8fP6czU6t9DJ/bNzsFqggd3K+Ftqufru5M//fXEQD0sBpU6TibfZz9E7ICLvv/8+OTk5+Pn5uXeYKCkpYebMmdx2223Y7XZq1aqF1XrynWJGjRrFyy+/zOrVq/niiy8YPXo0AE6nk5KSEkpLSzFNk6KiIiwWC15elW/6vdgoMBYRqYH+fkftSmUTuh87QB1xY61KZQ1re7Dp2dAT9lHbZmFufOXdKi4P8eSz8p0vROTcaty48THLP/jgAx5//HHKysq4/PLL+fDDD0/YTmhoKIGBgYSFheHr68s777xD06ZNAfj+++/p0qWL+1ybzUZMTAxLly6tsnlcqAzTNKur7WprWETkYrU3Mex8D0FEzoP6icrvPUuV80XOgG6+ExERERFBgbGIiIiICKDAWEREREQEUGAsIiIiIgLo5jsRERERqfl0852IiIiISFVRYCwiIiIiggJjERERERFAT74TEbmg6AEfIpcOPdTjwqMVYxERERERFBiLiIiIXLCSkpLo2LFjlZ8rx6bAWEREROQCExkZyeLFiyuVjxgxgquuugoPDw8SExPP/cAucsoxFhGpYV5YeJB16aWUmfBQez96XWVz1+3McZAw/yAWAwwDJveqTVgtKw9/nEN6XhlOEwZe68s9rXxJy3Vw05RMrqjnCYDVAnMGBlfoyzRNnllwkNSsMnw84bUetWlgtzI75TCTlhUQbrcC8Nadtalfy3ru3gSRS1R0dDQTJ07knXfeOd9DuSgpMBYRqUE27y9lywEHXzxQh4JiJzdOyawQGCetOUzfq23c3cqX2SmH+ffqQ7x0Yy2e7RpAVLAHRQ6Trv88wO1Xuq65qr5npWD4aF/9XozVYvDp4GB+2V3Cy8l5vH1nIAB9r7aR0DmgeicsIhUMGjQIgJkzZx6z3jRNhg0bxowZM6hfvz5vv/023bp1O5dDrNGUSiEiUoPUC7DiaYXSMpOCEpPatoof45fX9SCvyPXg0dxCJ3X8XPVRwa51EE8LrtXkU+xve5aDFmGuFeVWDTxZtaPEXffJ2kJu/3cmE5fk46y+p6iKyGlYvXo1UVFRZGZmMnr0aO68806ys7PP97BqDAXGIiI1SG0fg0ZBHnR86wA3vpPJk538K9R3ivLmg58P0+1fB/jg58P0a+1boX7yDwXcfqUNbw9XaLx+bym9k7LonZTFY/NyKvXXLMSDZanFmKbJkq3FZB92AhB3uQ/LHqvLvPhgdh8sY966wmqasYicjpCQEBISEvD09OSee+7h8ssv58svvzzfw6oxlEohIlKDLNteQkZ+GSuG1SWv2KTXtCy6RHu7A93xi/N4tqs/tzaz8en6QiYk5zOhux2Aj9ceZvMBB//qXdvd3rFSKQbOyuZQicngtr7cdoWNn3eX0nt6NleFetIkxPW1cfRK9e1X+rB0WzF3tazu2YvIyTRo0ADD+PM3oYiICNLTtV/yqVJgLCJSk5hg97FgtRj4e7lSKsrMCtUE+bqC1jp+FnKLXCu8X20u4tP1RST1DcRinDiRYka/oArHz3Rx5REvTS3Go/z+uoNFTuw+rn6W/1FC4zr6OhG5EOzZswfTNN3B8a5du+jZs+d5HlXNoU8yEZEapHNjL+ZvcOX2lpTB4LZ+bM9y8H1qMY928Cehkz//98VBPCwFlDpNJt7mWi1+fF4u0XU86PuBK9fwrTtdq8ZHUimOmNEvED+vP1eDcwud3D87B6sFGtitjL+lFgD/+rGAH/4owWqBxsEevNBNN+GJnAulpaWUlZXhdDpxOBwUFRXh6emJ1er6q3X//v1MnjyZRx99lPnz57Np0yZuvfXW8zzqmkOBsYhIDWIxDP5+R+1K5VeGum6QuzzEk8+G1KlUv+2F0GO2t+nZY5cfUdtmYW585V0rnutWi+dOZcAiUqUefPBBpk+f7j4eP34806ZNIz4+HoB27dqxdetW6tSpQ7169fjkk08IDj7+zjNSkWFW353EukVZROQ07U0MO99DEJFzpH6icn+r0KlutnNC2pVCRERERAQFxiIiIiIigAJjERERERFAOcYiIiIiUvMpx1hEREREpKooMBYRERERQYGxiIiIiAigB3yIiFxQtI+xyMVNexdf2LRiLCIiIiKCAmMREREREUCBsYiIiMgFZfny5Vx//fXY7XaCgoLo0KEDa9asYe/evfTs2ZOwsDAMw2DHjh0VrisuLmbIkCHUqlWL0NBQJk2aVKE+OTmZpk2b4uvrS5cuXdi5c+cpj2nq1KlER0fj7+/PzTffTHr6xZkSosBYRKSGiRq/l95JWfROymLWL4cr1BU5TB6bl8Md0zJ5bF4ORQ7XlvJpuQ76TM+i578zmfxDwTHb6p2URdahskr9vfpdPj3ez6R3Uha/7SsF4OO1h+k+NZNe07J45JMcih3aul6kKuTl5XHbbbcxbNgwsrOz2bNnD6NGjcLb2xuLxcLNN9/M3Llzj3ltYmIiW7duZefOnXz33XdMnDiRr776CoDMzEzuvPNOxo4dS3Z2Ntdeey333HPPKY1p2bJlvPDCC3z22WdkZ2fTqFEj+vbtW2VzvpDoAR8iIheQU7n57vrJ+1nxRMgx62b89xBZh5w8FRPApGX51PGzMPBaPx75JIfBbfxoF+HF3TOyGH+rnb/U8ThhWwAbMkqZkJzPzP5B7DlYxpPzc/lkUDA7cxyE261YLQZjv82jcbAH/Vr7nvG8RS4VJ7v57r///S+xsbHk5uYe9xyHw4Gnpyd//PEHkZGR7vIGDRowbdo0brrpJgBGjBjB1q1b+c9//sO7775LUlISK1asAODQoUPUqVOHX3/9laZNmzJt2jQmTpzI7t27qVu3Ls8++ywPP/wwAMOHD6ewsJC3334bgPT0dBo0aMC2bdto3Ljx2bwdVUkP+BARuRTtL3ByZ1IW98/OJi3XUaFuxY4SYpv4AHBjEx9W7SwBYGNGKe0ivACI/Ys3q8vLT2Z7loMW9T0BaGC3siunjGKHSUSgB1aL63vIy2rgoW8TkSrRpEkTrFYrgwYNYtGiReTk5JzSdTk5OaSnp9OyZUt3WcuWLdm4cSMAGzdurFDn5+dH48aN3fUhISF88cUX5OXlMW3aNJ566il++eUXAEzT5OiF1COvN2zYcHaTvQDpo0xEpIZZnRDCvPhgBlzjx18/P1ihLrfQSW2bK2C1+xjkFDoBOPrHwVo+FnIOu8oz8svcaRR3z8iq1FfTEA9W7CimpMxkY0Ype/PKOFjkdNdvPVBK8tYiel5pq+ppilySatWqxfLlyzEMgwcffJC6devSs2dP9u3bd8LrCgpcKVJ2u91dZrfbyc/Pd9cfXfe/9d27d6dx48YYhkFMTAw33XQTP/zwAwC33norc+bMYd26dRQWFjJmzBgMw+Dw4YqpXBcDBcYiIjVMsK/ro/uGaG9251bMCa5ts3CwyBUF5xWb1La5zjWO+pExv9h0B8+hAVbmxgczNz6YOQODAfjr57n0Tsri3z8dokldT3pdZePeD7KZuvoQl4d4uPtPzysj4bODvNsnEB+PKvkVU0SAZs2akZSUxO7du9mwYQPp6ekkJCSc8Bp/f3/AlaN8RF5eHgEBAe76o+v+t37RokW0b9+eoKAgateuzcKFC8nMzASgW7dujB49mt69exMREUFkZCQBAQGEh4dX2ZwvFAqMRURqkEMlTsqcrsD3t32lBPlW/Bi/LsKLJVuLAViytZjrytMnrgj1ZE2aK31iybYi2peXH8vrPWszNz6YIW39AIhv48e8+GAevs6PpiGuFIqsw04enJPDhO52IoP0rCiR6tK0aVPi4+NPmrYQGBhI/fr1Wbt2rbts7dq1NG/eHIDmzZtXqDt06BCpqak0b96c4uJievfuzfDhw9m3bx+5ubnceuutFdInHnvsMbZu3cr+/fvp3bs3DoeDK6+8sopne/4pMBYRqUG2HHBw83uu3SBeWpTHK7fZ2ZBRyj9/dP2MencrXzbtL+WOaZls2l/K3a1cN8S90C2AV5bk0/PfmbS7zIu/1HXlDR+dStE7KYs/sh2V+rz3gyz6TM9i0rICEuNqAfD60nwy8soY/XXeMXfHEJEzs3nzZl5//XV2794NQFpaGh999BHt27cHoKioiOJi1x+/xcXFFBUVua8dOHAg48aNIycnh82bN/Pee+8RHx8PQK9evdiwYQNz586lqKiIMWPG0KJFC5o2bUpJSQnFxcXUrVsXDw8PFi1axDfffONut6ioiA0bNmCaJrt27eKhhx7iySefJDAw8By9K+eOdqUQEbmA6JHQIhe3k+1KsWfPHp566il+/PFHcnNzqV27NrfddhuvvvoqtWrVwjAqpy0dieWKi4sZOnQon3zyCTabjWeffZann37afd7ixYt5/PHH2blzJ+3atSMpKcm9q8Xbb7/NmDFjKC4upkePHpSWlhIdHc24cePIzc2lc+fOpKamEhAQwODBgxk3bhxWq7Xq3pizVyX5XAqMRUQuIAqMRS5uJwuM5YxpuzYRERERkaqiwFhEREREBAXGIiIiIiKAcoxFREREpOZTjrGIiIiISFVRYCwiIiIiggJjEREREREA9BxPEZELiPYxFrl4aQ/jC59WjEVEREREUGAsIiIiIgIoMBYRERG5pGzbtg3DqJLdzS46CoxFREREzjN/f3/3P4vFgs1mcx/PnDnzfA/vkqGb70REapD8Yif9PszGy2pQWGryfLcAOkV5u+t35jhImH8QiwGGAZN71SaslpWkNYeYuuoQThNWPBHiPj9q/F6ubuDlPn63T22C/awV+nz1u3y+316Ml9Vg7C21uKKeJzuyHTz8SQ6pmWXMvC+Idpd5ISJnrqCgwP06MjKSqVOnEhsbe0ZtORwOPDwU4p0JrRiLiNQgfl4Gnw4OZm58MP+6qzYvJ+dXqE9ac5i+V9uYGx9Mn5Y2/r36EADdm/nw3aN1K7UXGmBlbnyw+9//BsUbMkpJSS9lwf11mNyrNiO/ygMgJMDKfwYEc9sVPtU0UxE5Ys+ePfj6+pKbm+suW716NaGhoTgcDqZOnUrnzp154oknCAoKYty4cTidTsaMGUNERAQhISHEx8eTl5dXod333nuPsLAwwsLCeOONN9zlL730Evfccw+XTNqWAAAgAElEQVR9+vQhICCAa6+9lvXr15+z+Z5PCoxFRGoQi2HgYXHlBuYXmzQLqbgqdHldD/KKTAByC53U8XN9zNf1t+JpPf2cwu1ZDlrU9wSggd3Krpwyih0mvp4GgTZ9hYicCw0aNKBjx458/PHH7rIPP/yQvn37uleGV6xYQbNmzThw4ADPPvssU6dO5cMPP2Tp0qWkpqaSk5PDk08+WaHd77//nm3btrFo0SLGjRvH0qVL3XXz5s2jX79+ZGdnc9ddd9GrVy8cDsc5me/5pE81EZEaZm9eGbf/O5O+H2RzS7OKK7adorz54OfDdPvXAT74+TD9WvuesK2M/DJ6J2XROymLu2dkVapvGuLBih3FlJSZbMwoZW9eGQeLnFU6HxE5uUGDBvHhhx8CrlSJ2bNnM2DAAHf9ZZddxtChQ7FardhsNmbOnMnw4cNp1KgRAQEBvPzyy8yaNQun88//v6NGjcLX15eWLVsyaNAgPvroI3ddu3bt6NWrF56enjzzzDPk5eWxZs2aczfh80QJKCIiNUz9WlY+G1KHtFwHvZOyubHJn8Hx+MV5PNvVn1ub2fh0fSETkvOZ0N1+3LaOpFIc7a+f57Iju4zuV/gwpK0fva6yce8H2UQEWrk8xINgX62piJxrvXr14rHHHmPXrl2sW7eOunXr0rp1a3d9w4YNK5yfnp5ORESE+zgiIoKSkhIOHDhwzGsiIiJYvHjxMeusVisNGjQgPf3if0CJAmMRkRqk2GHi7eFKifD3tuDvXTE9wgSCygPXOn4Wcs9gdff1nrUrHMe38SO+jR+b95fy1vICrBZt8yRyrvn6+tK7d29mzpxJSkpKhdVioNL2a2FhYezcudN9vGvXLry8vKhbty75+a57E9LS0oiOjnbXh4X9+eTNtLQ092un08mePXsq1F+sFBiLiNQgv+93MOrrPKwWKC0zGR1Xiw0ZpXyfWsyjHfxJ6OTP/31xEA9LAaVOk4m3uVaLF2ws5IOfD5ORX8bdM7J4pksAbRp6uVMpjnitp51GQRW/Gu79IIsyJwT6Wnj51lqAa3eM+2fnsPWAg98POOga7c0zXQLO3RshcgkaOHAgDzzwAHv37uX1118/4bl9+/Zl0qRJ3HTTTQQHB/Piiy/St29fLJY/f/EZO3Ys77zzDtu2bWP69OnMmTPHXffTTz/x2Wefceutt/LGG28QEBBAmzZtqm1uFwoFxiIiNUiLME8+HRxcqfzKUNcNcpeHePLZkDqV6ns0t9Gjua1S+fYX65+0z/8MqNxfgLeFOQMrl4tI9encuTNlZWW0a9eO8PDwE5774IMPsnfvXjp16kRRURG33HILb775ZoVzOnbsSFRUFADPP/88Xbt2ddf16tWLDz/8kPvuu48mTZowb968S2ILOMM0zepqu9oaFhG5WO1NvPh/qhS5VNVPPPsc3c6dOzNkyBDi4+PPfkDH8dJLL7F7926SkpKqrY9qUCU5XrqDQkRERKQGWLVqFRs2bKBPnz7neygXLQXGIiIiIhe4/v37c/PNN/Pmm2/i5+d3vodz0VIqhYjIBUSpFCIXr6pIpZDjqpJUCgXGIiIiIlLTKcdYRERERKSqKDAWEREREUGBsYiIiIgIoAd81Gj3ztJ2LSIXm//0+/h8D0FE5JKlFWMRERERERQYVxvDMNi2bdv5HoaIiIiInCIFxicQGRmJzWbD39+f0NBQ4uPjKSgoOO12vvvuO7p06YLdbicyMrJC3f79++nbty9hYWHY7XY6dOjA6tWrq2gGIiIiInKqlGN8EgsWLCA2NpaMjAzi4uKYMGEC48ePP602/Pz8GDJkCH379uXll1+uUFdQUECbNm2YNGkSISEhvP/++3Tv3p0dO3bg7+9flVMRuaREBkYy+Nr7cZpOypxO3l39L/Yf2u+u9/Py57HrHsfX05cdOTtI+vnfANzd4h46NYohI38v45eMBaCuX10m3DKRXTk7AXCaTsYtGVOhP6vFg8evG0agLRAPiwcfrZ3Fxn0bsPvYGdr+cTytHmQeyuS9n6bgcDrO0bsgIiKnQyvGpyg0NJS4uDhSUlIAiI+P55FHHuHGG28kICCAmJgYdu7cecxr27Zty4ABA4iKiqpUFxUVxdNPP039+vWxWq089NBDlJSU8Pvvv1frfEQudjmFuUz4bjyjF4/ii82f06fFPRXqeza7nZU7V5C4eCTeHt60rN8KgG+2fM3YxYmV2vsjeztjkhMZk5xYKSgGaBnagmJHMYmLR/Lmj2/Qt2U/AG6/ohfL/viOscmj2XNwN50bxVT9ZEVEpEooMD5Fu3fvZtGiRURHR7vLZs6cyYgRI8jMzKRVq1b079//rPtJSUmhpKSkQj8icvoOFuVS5CgCwFHmoMxZVqH+inpX8MuenwH4Zc/PNAtpBkBuUS7mGTy4M6NgH55WT8C1Gn2w+CAA9WvVZ3vWdgC2ZW2jeb0rz2xCIiJS7ZRKcRJ33HEHhmFQUFBA165dGT16tLuue/fudO7cGYDx48djt9tJS0ujYcOGZ9RXXl4eAwYMYNSoUdjt9ioZv8ilztvqzb0t+/KvVf+sUO7n5c+h0kMAHCo5hL9XwAnbaRQUxchuiQBkF2bz1orJFer3F+zD0+rFpNvexNfTl1e/fwWAtNxdtAprxddbvuLqsNb4eSlFSkTkQqXA+CTmz59PbGwsy5Yto1+/fmRmZlK7dm2ACgGwv78/QUFBpKenn1FgXFhYSI8ePWjfvj3PP/98lY1f5FJmNaw82fEp5v/2KXvydleoO1RSgK+nL4dLD+Pr5UtByYlvrP0je7s75/iI/4t5Dh8PH77e8hX+Xv5kHc5k0g+vUtevLk93Gs7zXz3Lpxs/Zci199Omazt25e4gpzC7yucpIiJVQ6kUpygmJob4+HiGDx/uLktLS3O/LigoIDs7m7CwsNNuu7i4mDvuuIMGDRowZcqUKhmvyKXOwODx659gze6f+O/uNZXqN+3/javDWgNwdVhrNu3/7bT7mLjsb4xJTmR12iowIL84H4CCkkP4eNoAKCw9zNsr/8G4JaMpKStxnSsiIhckrRifhoSEBCIjI9034C1cuJDly5fTtm1bRowYQbt27Y65Wux0OikpKaG0tBTTNCkqKsJiseDl5UVpaSl33XUXNpuNGTNmYLHobxWRqtC2YTuuDmuN3cdOp8jO7Mrdxa/pv1DLuxY/7Piez3/7jEevG8aNf7mJnbk7Wbd3LQBxTW7muss60MDegBe7jmDqT+/iNJ0VUikAXln2N4rLc5gBlv/xA8M6JDCy22i8PbyYvfYjAJrXu5I7r+yNaZpsyFhPSvqv5/R9EBGRU2eY5unfZHKKqq3hcyUyMpKpU6cSGxvrLhs6dCj79+8nICAAHx8fUlNTWblyJa1bt2b69Ok0atQIcD3gY+vWrURHR7N06VK6dOlSoe2YmBiWLl3KsmXLuOGGG7DZbBWC4kWLFtGpU6cTjk+PhBa5+OiR0CIiZ8SokkYUGJ+Z+Ph4wsPDGTdu3HkbgwJjkYuPAmMRkTNSJYGxfrcXEREREUGBsYiIiIgIoJvvzlhSUtL5HoKIiIiIVCHlGIuIiIhITaccYxERERGRqqLAWEREREQEBcYiIiIiIoBuvqvRtI+xyMVH+xiLiJw/WjEWEREREUGBcbUxDINt27ad72GIiIiIyClSYHwCkZGR2Gw2/P39CQ0NJT4+noKCgirvZ8SIEVx11VV4eHiQmJhY5e2LiIiIyMkpx/gkFixYQGxsLBkZGcTFxTFhwgTGjx9fpX1ER0czceJE3nnnnSptV+RS1ygwir6t+mG1WEnNSmVWyofuuiZ1mvBA24cJDQgl4fNhZBdmA3DjX27i1su7YxgWEhYMc58/4+6ZbMva6j5+Y/kk8ovzKvQ3+Nr7iQqKwmJY+HLzF6zY+SNeVi8eu24YAd4BFJQc4p1Vb3O49HA1z1xERM6EAuNTFBoaSlxcHCkpKQDEx8fj4+NDamoqq1atonXr1syYMYOIiIhK157s3EGDBgEwc+bMczchkYuc1eJB31b9mfTDqxQ5iirVpx3czYhvXuTZmOcqlP+Utpol25J5/ba/VyjPLsxmTHLicfsLtzck3B7OiG9exMfDh1dueZUVO3+kW3QsqdmpfP7bfK677Hp6NLud2es+qpI5iohI1VIqxSnavXs3ixYtIjo62l02c+ZMRowYQWZmJq1ataJ///7Hvf50zhWRs9ekThOKHEUM6/AkL3UdRdO6TSvUF5YepvgYAfPBooOUmWWn3V9OYQ4OpwOrYcXmaaOgxJV2VT8gjO1ZqQBsy9pG83rNz2A2IiJyLmjF+CTuuOMODMOgoKCArl27Mnr0aHdd9+7d6dy5MwDjx4/HbreTlpZGw4YNK7VzOueKyNkLtAUSERjBcwufwcfTxktdR/LXLxPOuL0gWxAjuyUC4DSdjFsypkL9oZICMvIzeKPHZLw9vHl3tSs1Ki13F63CWrFh33quDrsaPy//Mx6DiIhULwXGJzF//nxiY2NZtmwZ/fr1IzMzk9q1awNUCGr9/f0JCgoiPT39mMHu6ZwrImevoKSALQd+p9BRSKGjkPzifGp51yLvf/KCT9WxUikeavsIoQGhrE5bxd68dAJtQTy5YBi+nr4kxo5h7d4UlmxfwsDWgxjRbRRbM7eQU5hTBbMTEZHqoMD4FMXExBAfH8/w4cOZP38+AGlpae76goICsrOzCQsLO+b1p3OuiJy9bZlbubvFvVgMC15WL+w+tcgvqdpdZd796c8bZlvUb8mhkgJM00lRaSEeVg8shoWSshKm/fd9ALo1jiXrcFaVjkFERKqOAuPTkJCQQGRkpPsGvIULF7J8+XLatm3LiBEjaNeu3XFXgE90bmlpKWVlZTidThwOB0VFRXh6emK1Ws/Z3EQuNodLD/P174sY2W00HhYrs1Jmcpn9Mq6q34IvNn1O/YD6DGnzAJcFRvBEhwR+3LGcb7d9Q7uG7Yn9y40E2gJ5sesIPl43my2ZWyqkUgBMWf0O+woy3Mfr966jQ0RHEmPH4mn14OvfF1FSVkKDWuHc3+YBnKaTXbk7+fDXD87DuyEiIqfCME2zutqutobPlcjISKZOnUpsbKy7bOjQoezfv5+AgAD3ThMrV66kdevWTJ8+nUaNGgGuB3xs3bqV6OjoCrtSHOvc+Ph4pk+fXqHvadOmER8ff8Lx6ZHQIhcfPRJaROSMGFXSiALjMxMfH094eDjjxo2r0nNPhwJjkYuPAmMRkTNSJYGxtmsTEREREUGBsYiIiIgIoFSKGk2pFCIXH6VSiIicEeUYi4iIiIigHGMRERERkaqjwFhEREREBAXGIiIiIiKAAmMREREREUCPhK7xtDOFyMVFu1KIiJw/WjEWEREREUGBsYiIiIgIoMC4ksjISGw2G/7+/oSGhhIfH09BQcFZtfndd9/RpUsX7HY7kZGRlepTUlLo1KkTdrud8PBwxowZc1b9iYiIiMjpU47xMSxYsIDY2FgyMjKIi4tjwoQJjB8//ozb8/PzY8iQIfTt25eXX365Un2/fv3o1asXS5cuZceOHXTs2JFWrVrRs2fPs5mGyCXt+S4v0igwikW/f8mnG+dVqOvRrCftGranzCzjj+w/SPr53wDc3eIeOjWKISN/L+OXjAWgrl9dJtwykV05OwFwmk7GLan4x6vV4sHj1w0j0BaIh8WDj9bOYuO+DYT4hTD0uscwTRMTk7dX/IPswuxzMHsRETkTWjE+gdDQUOLi4khJSQEgPj6eRx55hBtvvJGAgABiYmLYudP1ZWmaJk899RQhISHY7XZatGjBhg0bAGjbti0DBgwgKirqmP3s2LGD/v37Y7Vaady4MR07dmTjxo3nZpIiF6kpq/7FzF8/OGbdmt0/8dI3LzDq2xHYfexcWe9KAL7Z8jVjFydWOv+P7O2MSU5kTHJipaAYoGVoC4odxSQuHsmbP75B35b9ALipSRzfpS5hTHIi329fxs2X31J1ExQRkSqnwPgEdu/ezaJFi4iOjnaXzZw5kxEjRpCZmUmrVq3o378/AN988w3ff/89W7ZsITc3l9mzZxMcHHxK/SQkJDBjxgxKS0v5/fffWblyJbGxsdUyJ5FLxYlWZjPyM9yvHU4HZaYTgNyiXMwzeJp9RsE+PK2eAPh5+XOw+CAAaQfT8PPyc5V7+3Ow6OBpty0iIueOUimO4Y477sAwDAoKCujatSujR49213Xv3p3OnTsDMH78eOx2O2lpaXh6epKfn8/mzZtp27YtzZo1O+X+brvtNgYOHMhrr71GWVkZI0eOpE2bNlU+LxGp6IqQKwi0BbJp/28nPK9RUBQjuyUCroD7rRWTK9TvL9iHp9WLSbe9ia+nL69+/woAGzLW83yXl7ghqiueVk9e/Pr5apmHiIhUDa0YH8P8+fPJz89n6dKlbN68mczMTHddw4YN3a/9/f0JCgoiPT2drl278vjjj/PYY49Rr149HnroIfLy8k7aV3Z2NjfffDMjR46kqKiItLQ0vv76a/75z39Wy9xExOWy2pdxb6v+vPnjGyc99+hUiiNB8f/FPMfIbom0a9iemEY3kHU4k6e/eJIR37zAA20eBKBfq/uYvfYjnl00nE/Wz+Heln2rdU4iInJ2FBifQExMDPHx8QwfPtxdlpaW5n5dUFBAdnY2YWFhADzxxBP8/PPPbNy4kS1btvDqq6+etI/t27djtVoZOHAgHh4ehIeHc++997Jw4cKqn5CIAFDPP5SH2z3K5OVvkF+cf0ZtTFz2N8YkJ7I6bRUYuNspKDmEj6fNfd6R8ryig/h7+Z/94EVEpNooleIkEhISiIyMdN+At3DhQpYvX07btm0ZMWIE7dq1o2HDhqxZswan00nr1q3x8/PDx8cHq9UKgNPppKSkhNLSUkzTpKioCIvFgpeXF02aNME0TWbNmsW9997L/v37mT17Nl27dj2f0xap8R5s+zBN6lyOp9WTqKDGfLL+Y66q34IvNn3OoGvi8fPy49HrHgdgwabP+TX9F+Ka3Mx1l3Wggb0BL3YdwdSf3sVpOiukUgC8suxvFDuK3MfL//iBYR0SGNltNN4eXsxe+xEAn26cywNtHsZplmG1WHnvp3fP6XsgIiKnxzDN07/R5BRVW8PVKTIykqlTp1a4+W3o0KHs37+fgIAAfHx8SE1NZeXKlbRu3Zrp06fTqFEjkpOTeeqpp9i+fTs+Pj7ExcUxZcoU/P39Wbp0KV26dKnQT0xMDEuXLgVgyZIlPPvss2zZsgWbzUaPHj1488038fX1Pel49UhokYuLHgktInJGjCppRIHxqYuPjyc8PJxx48ad76G4KTAWubgoMBYROSNVEhgrx1hEREREBAXGIiIiIiKAbr47LUlJSed7CCIiIiJSTZRjLCIiIiI1nXKMRURERESqigJjEREREREUGIuIiIiIALr57qKgvYxFLh7ax1hE5PzRirGIiIiICAqMq51hGGzbtu18D0NERERETkKB8SmIjIzEZrPh7+9PaGgo8fHxFBQUnHY7f//734mKiqJWrVqEhYXx1FNP4XA4ANi/fz99+/YlLCwMu91Ohw4dWL16dVVPRURERESOQznGp2jBggXExsaSkZFBXFwcEyZMYPz48afVRo8ePYiPj6d27dpkZ2dz1113MXnyZJ5++mkKCgpo06YNkyZNIiQkhPfff5/u3buzY8cO/P39q2lWIhcvm4eN57u8iMPpwMvDm/+kzGTDvg3ueruPnaHtH8fT6kHmoUze+2mK61yrF/HXDCHEPwSLYeH171/lUOkhZtw9k21ZW93Xv7F8EvnFeRX6HHzt/UQFRWExLHy5+QtW7PyRDhEd6RYd6+5z98HdvLH89XPzJoiIyGlRYHyaQkNDiYuLIyUlBYD4+Hh8fHxITU1l1apVtG7dmhkzZhAREVHp2saNG7tfm6aJxWJxp1lERUXx9NNPu+sfeughhg8fzu+//84111xTzbMSufgUOYpIXDwSp+kkxC+EJzs+xYtfP++uv/2KXiz74ztW7lxBz2a307lRDEtSk7nrqj6s2rWCdRnrKrSXXZjNmOTE4/YXbm9IuD2cEd+8iI+HD6/c8iordv7IjzuX8+PO5QAMufYBNh34rVrmKyIiZ0+pFKdp9+7dLFq0iOjoaHfZzJkzGTFiBJmZmbRq1Yr+/fsf9/pZs2ZRq1Yt6tSpw9q1a3n44YePeV5KSgolJSUV+hGRU2di4jSdANg8fdmVu6tCff1a9dmetR2AbVnbaF7vSgCa17uKlmFXM7JbIndddfcp95dTmIPD6cBqWLF52igoqZhuZTWstAq7mv/u/u/ZTEtERKqRAuNTdMcddxAQEEDDhg0JCQlh9OjR7rru3bvTuXNnvL29GT9+PCtXriQtLe2Y7fTr14+8vDy2bNnCI488Qr169Sqdk5eXx4ABAxg1ahR2u73a5iRysQu0BZEYO5YXur7EmrSKOftpubtoFdYKgKvDWuPn5UpZuqz2ZWzMWM+Y5ETC7eG0rO86J8gWxMhuiYzslshLXUdW6utQSQEZ+Rm80WMyf7vlVeZtmFuhvlXY1Wza/xulZSXVMVUREakCSqU4RfPnzyc2NpZly5bRr18/MjMzqV27NgANGzZ0n+fv709QUBDp6ekVyv/XX/7yF5o3b86jjz7KvHnz3OWFhYX06NGD9u3b8/zzzx/3ehE5uZzCbBIXj6CuX11Gdkvkl89/cdd9uvFThlx7P226tmNX7g5yCrMBKCgpIGWvK1Vq7d4ULqt9GWv3phwzleKhto8QGhDK6rRV7M1LJ9AWxJMLhuHr6Uti7BjW7k3B4XTdYNsxshNLUpPPzcRFROSMaMX4NMXExBAfH8/w4cPdZUevDhcUFJCdnU1YWNhJ23I4HKSmprqPi4uLueOOO2jQoAFTpkyp2oGLXGI8LH/+3X+4tJBCR1GF+sLSw7y98h+MWzKakrISVqetAuC3fRuJCnLdDxAV1JiM/Izj9vHuT+8wJjmRr7d8BYbBoZICTNNJUWkhHlYPLIbrI9bmYSMqKIoNGeurepoiIlKFtGJ8BhISEoiMjHTfgLdw4UKWL19O27ZtGTFiBO3atTvmavHUqVPp2bMnISEh/Pbbb0yYMIG4uDgASktLueuuu7DZbMyYMQOLRX+ziJyNhvbLGHjNIJymE6vhwYyfpxFRO5Kr6rfgi02f07zeldx5ZW9M02RDxnpS0n8F4KOUmTzU7hE8rZ5k5Gfw391rgD9TKY6Ysvod9hX8GTSv37uODhEdSYwdi6fVg69/X0RJedpEu8vas2b3GkzMc/cGiIjIaTNMs9o+qC+ab4DIyEimTp1KbGysu2zo0KHs37+fgIAA964UK1eupHXr1kyfPp1GjRoBrgd8bN26lejoaAYPHszChQspKCigbt269OnTh7Fjx+Lj48OyZcu44YYbsNlsFYLiRYsW0alTpxOOT4+EFrl46JHQIiJnxKiSRhQYn534+HjCw8MZN27ceRuDAmORi4cCYxGRM1IlgbF+rxcRERERQYGxiIiIiAigm+/OWlJS0vkegoiIiIhUAeUYi4iIiEhNpxxjEREREZGqosBYRERERAQFxiIiIiIigG6+ExG5oOxNPPnj5EWkZqifmH6+hyCnSSvGIiIiIiIoMBYRERERARQYi4iIiIgACoxFREREzovly5dz/fXXY7fbCQoKokOHDqxZs4a9e/fSs2dPwsLCMAyDHTt2VLiuuLiYIUOGUKtWLUJDQ5k0aVKF+uTkZJo2bYqvry9dunRh586dpzymOXPm0KxZMwICArjiiiv4//buPD6q6v7/+OtkIfsKgYAsAYKyKCAiSEU28xMtgqC4EKwMKkWoKFi+UhcwIHyp+q2iVetCNdCCWkUpKCiigqCArZiwCwSEhLCFEMMEspH7+2MuwwQSCGECCXk/H495cOece8+Wm+GTM+feO3/+fG90tcZQYCwiUsOkHSqm6bN7WbO7sFT6m6ucDHr3EAPeyeKRT3IoOn7yOUtFxy2u/+sBZnx7BIDvfymg41/2c0fyIe5IPsQfPj58Wj05x0q45x+HuD3ZVeam/UUAHC+xmLwkl7tmu47derCoCnsrcmnKzc3l1ltvZcyYMWRnZ7Nnzx6eeeYZAgIC8PHx4eabb2bevHllHpuUlMS2bdvYtWsX33zzDc8//zyff/45AFlZWdx+++08++yzZGdn07lzZ+6+++4KtWnPnj3ce++9vPjii+Tm5vLCCy+QmJjIgQMHvNbv6k53pRARqWFmfOvkumZ1Tksf3iWEkd1CAXjkkxyWpxWQcHkgAP/48Sgt65X+yL+xVQB/GRBZbj0frz/GtU3q8MdeYXz/SwEvr3Dy5uAo/vnjUVrW9eOZm8K92CuR2mXr1q0ADBkyBICgoCBuuukmd/7o0aMpLi4u89jZs2fz7rvvEhUVRVRUFCNGjCA5OZmbb76Zjz/+mHbt2nHnnXcCriC6Xr16bNmyhdatW/Puu+/y/PPPk5GRQUxMDBMmTGDkyJEAZGRkEBkZyS233AJAv379CAkJIS0tjfr161fZWFQnmjEWEalBftpTSEyID43CfU/Lq+PreiKqZVmUWBZx0a5AOK+whG+2F/Db1oHnVFeren44C1yzzjnHSqgX7Pov49NN+WTkFDN41iGeXPQrhR4z0yJSMZdffjm+vr4MGzaMxYsXc/jw6d/alOXw4cNkZmbSoUMHd1qHDh3YuHEjABs3biyVFxISQsuWLd359evX59NPPyU3N5d3332XcePGsXbtWgA6d+5MmzZtWLBgAcePH2f+/PkEBATQvn17b3W72lNgLCJSg8z41snD3UPLzX/52yN0f/UgOccsGkW4gufXv8vjwa4hGFN636+2FbiXUkz+Ive0sto38ufHPYX0fv0gExfn8tBvQgDYe+Q49cN8+WhYXQL8DO//dNR7HRSpJcLDw1m5ch0zLvwAACAASURBVCXGGEaMGEFMTAwDBgxg//79ZzzO6XQCEBER4U6LiIjgyJEj7nzPvFPz+/XrR8uWLTHG0LNnT2666SZWrFgBgK+vL/fddx+JiYkEBASQmJjIm2++SUhIiNf6Xd0pMBYRqSGWbs2nQyN/ooPL/+h+tEcYKx+OoUmkL/9KOUpW3nE27CuiZ8uA0/a9sVUA8xx1meeoyzN9w8krLHEHymt2F/L6d076tQnkm9ExvHlnFE8ucgXPUUE+9I53ldc7PoDN+8v+uldEzqxNmzYkJyeTkZHBhg0byMzMZOzYsWc8JjTU9Ydxbu7JP2Zzc3MJCwtz53vmnZq/ePFirrvuOqKjo4mMjGTRokVkZWUBsHTpUh5//HGWLVtGYWEhy5cv58EHHyQlJcVrfa7uFBiLiNQQG/cVseqXQhL/mc23Owp4dkkuGTkng9L8YteSBmMM4YGGIH/Dpv3FZB8tIfGf2by5Ko+PUo+x5Of8MssPqePjDpS7Nq2DZeEOwuuF+JBzrASAbs3qkJrpuuAuNbOIuOjTl3WIyLlp3bo1DoeDDRs2nHG/qKgoGjZsSGpqqjstNTWVdu3aAdCuXbtSeXl5eaSlpdGuXTsKCgq44447GD9+PPv37ycnJ4ff/va3WJbrsyMlJYUePXrQuXNnfHx8uPbaa+natStLly6tgh5XTwqMRURqiEd7hPHhsLrMvTeaHi0CmHhTONuyivko1bWUYfIXudyRfIhB7x7igLOE268KokeLABY+UI+590YzslsIgzsEcdMVrrXGnksp7pubfVp993cN4cPUY/ZdK3J44kbXjNPo60P59wZXesqeQn53TfCFGwSRS8SWLVv4y1/+QkZGBgDp6em89957XHfddQDk5+dTUFAAuG7Plp9/8g/a++67j6lTp3L48GG2bNnC22+/jcPhAGDQoEFs2LCBefPmkZ+fz5QpU2jfvj2tW7emsLCQgoICYmJi8PPzY/HixSxZssRd7rXXXsuKFSvcM8Q//fQTK1asqFVrjM2JvxKqgK7GEBE5R3uTGl3sJoiIlzRMyiw3b8+ePYwbN47vvvuOnJwcIiMjufXWW3nhhRcIDw/HnHpRALhndgsKChg1ahQfffQRQUFBTJgwgccee8y939KlS3n44YfZtWsXXbt2JTk5mbi4OABee+01pkyZQkFBAf3796eoqIj4+HimTp0KwKuvvsqMGTPYv38/MTEx/OEPf+CPf/yjF0elypw+YJUpRIGxiEj1ocBY5NJxpsBYvM4rgbGWUoiIiIiIoMBYRERERARQYCwiIiIiAmiNsYiIiIjUfFpjLCIiIiLiLQqMRURERERQYCwiIiIiAoDfxW6AeNc9c++82E0QkfPwfuKHF7sJIiK1lmaMRURERERQYOxVxhi2b99+sZshIiIiIpWgwPgUcXFxBAUFERoaSmxsLA6HA6fTec7lfPPNN/Tu3ZuIiAj388k99e7dm5iYGMLDw+nQoQP//ve/vdB6EREREaksrTEuw8KFC0lISGDfvn307duX6dOnM23atHMqIyQkhPvvv58hQ4bwv//7v6flv/zyy7Rt2xY/Pz/WrFlDQkICW7dupWHDht7qhkitN/uuOWw/tA2AFTu/5ZsdX5+2z+Cr7qJ73A2MXTgGgEk3JuHn60/x8SJ25+wm+cd3iAmJYfotz7P78C4ASqwSpn49pVQ5vj5+PNxtDFFBUfj5+PFe6lw27t9Qbj0iIlL9KDA+g9jYWPr27UtKSgoADoeDwMBA0tLSWL16NZ06dWL27Nk0a9bstGO7dOlCly5dWLp0aZllt2/f3r1tjKGoqIj09HQaNmxIWloaI0aMIDU1FWMMffv25bXXXiMyMrJqOipyico+ls2Ur5LKzY8IjKBh2Ol/jM5Y8Reyj2WXStuZvYNpXz9bblkdYttTUFxA0tJJxITE8Oj143h6yZNnrEdERKoXLaU4g4yMDBYvXkx8fLw7bc6cOUycOJGsrCw6duzI0KFDK13+rbfeSmBgIF27dqVXr1507twZAMuyeOKJJ8jMzGTz5s2kp6eTlJR0vt0RqXUiAyOZdONkHrthPDEhMafl337lYP696ZNSaRYWj1w/jqf7PEO7BldWuK59zv34+/oDEFInlF8Lfj1jPSIiUv1oxrgMAwcOxBiD0+mkT58+TJ482Z3Xr18/evToAcC0adOIiIggPT2dJk2anHM9n376KUVFRSxdupQtW7bg4+P6OyU+Pt4djMfExPDYY4+VaoOIVMyYBaM5UnCE9g07MLLrqFLLH2LDYgn0C2R3zu5Sx8xY+SJHCo5QN7guT/WZxJOfTwCgeXQLJt2YBLhmol/9/pVSxx1w7sfftw4v3voywf7BvPDtc2esR0REqh8FxmWYP38+CQkJLF++nMTERLKystzLGDwD4NDQUKKjo8nMzKxUYAzg7+/PLbfcwssvv0zLli0ZMGAABw4c4JFHHmHFihUcOXKEkpISoqKivNI3kdrkSMERANbtTeX+zg+Wyht81V18uO6Dco85dPQQuw7/QmxYLHmFeWUupXi8558I9Avki62fE1onlENHs3hxxQvEhMTw2A3jeeLzCeXWIyIi1Y+WUpxBz549cTgcjB8/3p2Wnp7u3nY6nWRnZ9OoUaPzrqu4uJi0tDQAnnjiCYwxrFu3jtzcXP75z39iWdZ51yFSmwT4BWKM6yOuaWRTjhTklsqvH9qA4Z0f5E+9niIyKJJh1wwHIMgvCIBAv0CaRjblYF5WuXU8v/zPTPkqiTXpq8GcDKqdhXkE+gedsR4REal+NGN8FmPHjiUuLs59Ad6iRYtYuXIlXbp0YeLEiXTt2rXM2eKSkhIKCwspKirCsizy8/Px8fGhTp06bNmyhZ07d9KrVy/8/Pz44IMP+Pbbb3n++ecBOHLkCBEREURGRrJnzx5eeOGFC9pnkUtB44jGPHjt78kvPoZlwds/vEWzyDiuatieTzcvYNKSp9z7zuj/V2b9+C6+xpeJCUkUFhfi5+PLR+s/JK/QSbB/UKmlFADPLf8zBcX57vcrd65gzPVjmXTjZAL86vBB6nsAZdYjIiLVk6nCmcgaOcUZFxfHzJkzSUhIcKeNGjWKAwcOEBYW5r4rxapVq+jUqROzZs2iefPmgOvuEtu2bSM+Pp5ly5bRu3fvUmX37NmTZcuWsXnzZhwOB5s2bcLX15dWrVrx5JNPMmjQIAA2btzIfffdx88//0x8fDy/+93veOmll8jIyDhr+/VIaJGaTY+EFhGpFOOVQhQYV5zD4aBx48ZMnTr1YjelXAqMRWo2BcYiIpXilcBYa4xFRERERFBgLCIiIiIC6OK7c5KcnHyxmyAiIiIiVURrjEVERESkptMaYxERERERb1FgLCIiIiKCAmMREREREUAX34mIVCt7k87/EfMicvE0TMq82E2Q86AZYxERERERFBiLiIiIiAAKjEVEREQumLi4OIKCgggNDXW/MjO9t/yisLCQwYMHExcXhzGGZcuWea3s2kBrjEVEapgW0/Zy9WV1ALijfRCJnYLdeW+ucrLk5wIAMn49zm9bB/JM33B3/u3Jh2ge7ctfBkSSnlPMTW9m0baBPwC+PvCv++qWqqvwuMWYj3PY7zxO0XF44sYwujcPcOe/8M0RPll/jO8fqV9l/RW51CxcuJCEhASvl1tcXAxA9+7dGTt2LHfeeafX67jUacZYRKSGiQ3zZZ6jLvMcdUsFxQAju4W681rV8+PWdoHuvC+35hMaUPoe+Fc19Hfvf2pQDLA8rYCgOob5w+vxxuBIpn91xJ130HmcHdnFXu6dSO20cuVKfvOb3xAZGUmTJk3cT9stKChg/PjxNG3alAYNGvDQQw9x7NgxAJYtW0bjxo157rnniI2NZfjw4dSpU4exY8fSvXt3fH19T6vn0KFD9O/fn/DwcK699lqefvppunfvfiG7Wq0pMBYRqWEOOEu4PfkQD3yQTXpO2YHpobzj7M4p5prGrpnlEssi+T9HGX5tcJn7lycuypfCYgvLsjh8zKJuyMn/Nl761smY7qGV74iIALB7925uueUWxowZw8GDB0lJSaFjx44ATJgwga1bt5KSksL27dvZs2cPU6ZMcR+7b98+srOz2bVrF2+99dZZ6/rDH/5ASEgI+/btY9asWcyaNavK+lUTaSmFiEgNs2ZsfeoG+7BsewF/XPBrmTO9n2zIp3/bIPf7f6Uc47etAwnwKz1jvH5vEXckHwIgNtyH126PKpXfNMqP/GKLG147SG6+xawhrvwdh4o5Wmi5l2GISMUNHDgQPz9XCNarVy+6du1KQkICQ4YMAaBu3brUrVsXy7J4++23WbduHdHR0QA8+eSTJCYmMn36dAB8fHyYPHkyAQEBZVfm4fjx48ybN48NGzYQHBxM27ZtGTZsmNYhe1BgLCJSw9QNds3a9ooP4MlFv5a5zyfrj/HXQZEA5BdbfLz+GHPvjeaH3YWl9ruqof9pgfV9c7PJK7QY3iWYnGMWjcJ9eefuaNJzinngg8MsGRnDX5Yf4X96hVVB70QuffPnzy+1xnj06NG0bNnytP0OHjzI0aNHueaaa9xplmVx/Phx9/uYmBgCAwNPO7YsBw8epLi4mCZNmrjTPLdFgbGISI2SV1hCoJ/B18ewaX8R0cGnr4hLO1SMAVrUdX3Epx8uJjff4r65h8k5VsJ+53HmrD1KjxZ1yqxjdmK0e/sf/81z1xER6ENeoQXA7sPHeXJRLgD7nSU8vfhXpt4S4c2uitQaTZo04YcffjgtvV69egQFBbFx40Yuu+yyMo81xpSZXpaYmBj8/PzIyMjg8ssvByA9Pb1yjb5EaY2xiEgNsvVgMTe/ncWgdw/x9OJcnrs1gg37inj9O6d7n3nrjjHoqpPLKFrF+PP57+sx995onv5/YfRqGcBQ+6K9E0spTrzyCktK1XdH+yBSM4u4PfkQd/8jmwl9XLPECx9wlTf33mgahPooKBY5D0OHDmXp0qX861//ori4mEOHDpGSkoKPjw8jRoxg3LhxHDhwAIA9e/bwxRdfnLG8goIC8vPzAdft2/Lz87EsC19fX26//XaSkpI4evQoW7ZsYfbs2VXev5rEWJZVVWVXWcEiIpcqPRJapGY72yOh4+LimDlz5mm3a1uxYgXjx49n8+bNREREMHXqVIYNG0Z+fj5Tpkzh/fffJysri8suu4xRo0bxyCOPsGzZMu69914yMjJOq2PXrl2l0nbu3ElcXBwHDx7E4XCwYsUKrrjiCvr06cN///tfvvrqK+8MwMVT8anzMxWiwFhEpPpQYCxSs50tMK5uJkyY4L5DRQ3nlcBYSylEREREaoktW7awbt06LMvihx9+4O9//zuDBg262M2qNnTxnYiIiEgtceTIEYYMGUJmZib169fnj3/8I7fddtvFbla1ocBYREREpJa49tpr2b59+8VuRrWlNcYiIiIiUtNpjbGIiIiIiLcoMBYRERERQYGxiIiIiAigi+8uSffMvfNiN0FEKun9xA8vdhNERGotzRiLiIiIiKDAWEREREQEUGB8wRljdP9AERERkWpIgXElxMXFERQURGhoKLGxsTgcDpxO5zmXM2PGDFq0aEF4eDiNGjVi3LhxFBcXl9rn5Zdfpnnz5oSEhNCmTRu2bt3qrW6IiIiIiAddfFdJCxcuJCEhgX379tG3b1+mT5/OtGnTzqmM/v3743A4iIyMJDs7m8GDB/PKK6/w2GOPATBz5kz+/ve/89lnn9GmTRt27NhBVFRUVXRH5JI0+Kq7aB/bnuKSYpJ/fIfdObvdeZfXu5wHu4wkNiyWsQvGkH0sG4DR3R6mXnA9AJpGNeP1Va+yds+PzL5rDtsPbXMf/9LKFzlSkFuqvuGdH6BFdAt8jA+fbfmU73d9R5v6bbmnwxCOl5RgUcLrq17l0NFDF6D3IiJyrhQYn6fY2Fj69u1LSkoKAA6Hg8DAQNLS0li9ejWdOnVi9uzZNGvW7LRjW7Zs6d62LAsfHx/3MouSkhImT55McnIybdu2PW1/ETmzZpFxxNeNZ9KXT1M3uC6juz3Ms19Nduen/5rBxCVPMaHnn0od9/qqVwHw9fHjpVtfZv3edQBkH8tmyldJ5dbXOKIJjSMaM3HJUwT6BfLcLS/w/a7v2Jq1lWe+nAhArxa9ufny3zIn5R9e7q2IiHiDllKcp4yMDBYvXkx8fLw7bc6cOUycOJGsrCw6duzI0KFDyz1+7ty5hIeHU69ePVJTUxk5cqS73IyMDDZs2ECTJk1o3rw5zzzzDCUlJVXeJ5FLQcPwhuzI3gHAoaOHqB9SHz+fk3MBx4qOUlCcX+7xnRp1YsO+9RSVFFWovsPHDlNcUoyv8SXIPwhnoWt51fGSk8ujgvyD2J2zqzLdERGRC0AzxpU0cOBAjDE4nU769OnD5MknZ6L69etHjx49AJg2bRoRERGkp6fTpEmT08pJTEwkMTGRbdu2MXv2bBo0aAC4AmOAJUuWsH79enJycrjpppto3LgxI0aMuAA9FKnZ0nPSufny3+Lr48dl4ZcRHVyXkDqh/JqfU6Hjb2jeg89/Xux+Hx0UzaQbkwAosUqY+vWUUvvnFTrZd2QfL/V/hQC/AN5a84Y77+pGnRh81V0E+wfx52XTz79zIiJSJRQYV9L8+fNJSEhg+fLlJCYmkpWVRWRkJECpADg0NJTo6GgyMzPLDIxPaNWqFe3atWP06NF8/PHHBAUFAfD4448TGRlJZGQkI0eOZNGiRQqMRSpgT24G3+1awVO9J7LfuY+MX9PJPWVNcHmC/YNpGtmMzQc2udPKWkrx+y4PERsWy5r01ezNzSQqKJpHF44h2D+YpIQppO5NobikmJ8y1/JT5lqua9qNezoM4eXvXvJmV0VExEsUGJ+nnj174nA4GD9+PPPnzwcgPT3dne90OsnOzqZRo0ZnLau4uJi0tDQArrjiCurUqYMxpmoaLlILfLltCV9uW0LjiCbc1nYgllWxpUjdmv6GH9JXY2Gdcb+3fjg5K9y+YQfyCp1YVgn5Rcfw8/XDx/jg7+PvXo5xtDCPwuOFle+QiIhUKQXGXjB27Fji4uLcF+AtWrSIlStX0qVLFyZOnEjXrl3LnC2eOXMmAwYMoH79+mzatInp06fTt29fAIKDg7n77rt5/vnnufrqq/n11195++23+Z//+Z8L2jeRmuzJ3k/jY3xxFh7hnf/MpFlkHFc1bM+nmxfQMKwh91/7IE2jmvHI9WP57peVfLl9CQDdm9/AO//5e6myPJdSALy55g32O/e536/fu47rm3UnKeFZ/H39+OLnxRQeL6R3yz7cENcTixKKS4p5e82bF6TvIiJy7oxlnXlG5DxUWcEXW1xcHDNnziQhIcGdNmrUKA4cOEBYWJj7rhSrVq2iU6dOzJo1i+bNmwOuB3xs27aN+Ph4hg8fzqJFi3A6ncTExHDnnXfy7LPPEhgYCEBubi6///3v+eyzz4iMjGTEiBFMnDjxrLPI98y9s+o6LyJV6v3EDy92E0REaiKvfMWuwNjLHA4HjRs3ZurUqRetDQqMRWouBcYiIpXilcBYt2sTEREREUGBsYiIiIgIoIvvvC45OfliN0FEREREKkFrjEVERESkptMaYxERERERb1FgLCIiIiKCAmMREREREUAX312SdB9jkZpL9zEWEbl4NGMsIiIiIoIC42rHGMP27dsvdjNEREREah0FxlUgLi6OoKAgQkNDiY2NxeFw4HQ6z7mcGTNm0KJFC8LDw2nUqBHjxo2juLi4ClosIiIiIlpjXEUWLlxIQkIC+/bto2/fvkyfPp1p06adUxn9+/fH4XAQGRlJdnY2gwcP5pVXXuGxxx6rolaLXDqe6P0UzaNasPjnz/hk48dl7jP4qrvoHncDYxeOASAuqjnDO9+PwfDV9qUs37mMmJAYpt/yPLsP7wKgxCph6tdTSpXj6+PHw93GEBUUhZ+PH++lzmXj/g30bzOArk2u47h1nJ3ZO0n+8Z2q7bSIiJwXBcZVLDY2lr59+5KSkgKAw+EgMDCQtLQ0Vq9eTadOnZg9ezbNmjU77diWLVu6ty3LwsfHR8ssRCrozdV/46rY9kQHR5eZHxEYQcOwhqXShne+n1e//yvZx7KZetM0/pvxHwB2Zu9g2tfPlltXh9j2FBQXkLR0EjEhMTx6/TieXvIk/8n4gYWbFwDw6PXjuLLBlWzYv8FLPRQREW/TUooqlpGRweLFi4mPj3enzZkzh4kTJ5KVlUXHjh0ZOnRoucfPnTuX8PBw6tWrR2pqKiNHjrwQzRap8bKPZZ8x//YrB/PvTZ+43/v5+BHgF8jBvAMcLylmy8EttKzb8gwlnLTPuR9/X38AQuqE8mvBr670I/vc+xSXFHPcKjnXboiIyAWkGeMqMnDgQIwxOJ1O+vTpw+TJk915/fr1o0ePHgBMmzaNiIgI0tPTadKkyWnlJCYmkpiYyLZt25g9ezYNGjS4YH0QuVTFhsUS6BfI7pzd7rSwgDCOFua53+cV5hEaEAZH9tI8ugWTbkwCXAH3q9+/Uqq8A879+PvW4cVbXybYP5gXvn2uVH7b+m2JCopi84FNVdcpERE5bwqMq8j8+fNJSEhg+fLlJCYmkpWVRWRkJECpADg0NJTo6GgyMzPLDIxPaNWqFe3atWP06NF8/HHZ6yVFpGIGX3UXH677oFSas8BJkH+w+32wfzDOQtdFs2UtpXi8558I9Avki62fE1onlENHs3hxxQvEhMTw2A3jeeLzCQA0jWzKPR2H8sLyP1dxr0RE5HxpKUUV69mzJw6Hg/Hjx7vT0tPT3dtOp5Ps7GwaNWp01rKKi4tJS0urknaK1Cb1QxswvPOD/KnXU0QGRTLsmuEUlRRReLyAusH18DW+XBHTmrSs8tf0P7/8z0z5Kok16avBwJGCIwA4C/MI9A8CoEFoLCO7juaVlS+580VEpPrSjPEFMHbsWOLi4twX4C1atIiVK1fSpUsXJk6cSNeuXcucLZ45cyYDBgygfv36bNq0ienTp9O3b98L3XyRGmlEl5FcXu8K/H39aRHdko/Wf8hVDdvz6eYFTFrylHu/Gf3/yqwf3wVg1o/v8sj1j2IwfLntC/KK8giuE1xqKQXAc8v/TEFxvvv9yp0rGHP9WCbdOJkAvzp8kPoeAMOucRBSJ4TR3R4GYOHmBfyUufYC9F5ERCrDWJZVVWVXWcHVXVxcHDNnziQhIcGdNmrUKA4cOEBYWJj7rhSrVq2iU6dOzJo1i+bNmwOuB3xs27aN+Ph4hg8fzqJFi3A6ncTExHDnnXfy7LPPEhgYeMb69UhokZpLj4QWEakU45VCFBhfWA6Hg8aNGzN16tQqq0OBsUjNpcBYRKRSvBIYa42xiIiIiAgKjEVEREREAF18d8ElJydf7CaIiIiISBm0xlhEREREajqtMRYRERER8RYFxiIiIiIiKDAWEREREQF08d0lT/c0FqlZdB9jEZGLRzPGIiIiIiIoML6ojDFs3779YjdDRERERFBgfN7i4uIICgoiNDSU2NhYHA4HTqfT6/X88ssv9O7dm+DgYFq3bs3SpUu9XoeIiIhIbaY1xl6wcOFCEhIS2LdvH3379mX69OlMmzbNq3UMGTKEbt26sWjRIhYtWsTgwYPZtm0bMTExXq1H5FIR5BfEE72forikmDp+AbyfMocN+ze48yMCIxh13cP4+/qRlZfF2z+8SXFJMTEhMYzsOhp/Xz9+2rOW+Zs+AWD2XXPYfmib+/iXVr7IkYLcUnUO7/wALaJb4GN8+GzLp3y/6zsAHNfcT1xUHEeLjvLaqlfJK/T+H88iInL+FBh7UWxsLH379iUlJQUAh8NBYGAgaWlprF69mk6dOjF79myaNWt22rFn2nfr1q2sXbuWJUuWEBQUxB133MGMGTOYN28eDz300IXupkiNkF+cT9LSSZRYJdQPqc+j3cfx1BdPuPNvazuI5Tu/YdWu7xnQ5jZ6NO/J12lfMaTjUD5a/wFbDm7hqT4T+SFjDZm5mWQfy2bKV0nl1tc4ogmNIxozcclTBPoF8twtL/D9ru/o0LAjAX4BJC2dxA3NezCgzW28lzrnAoyAiIicKy2l8KKMjAwWL15MfHy8O23OnDlMnDiRrKwsOnbsyNChQ8s9vrx9N27cSIsWLQgLC3Pv26FDBzZu3Fh1nRGp4SwsSqwSAIL8g9mds7tUfsPwhuw4tAOA7Ye2067BlQA0i4xjy8EtAPy0Zy1tYtpWqL7Dxw5TXFKMr/ElyD8Ipz0r3LZ+O9bu+RGAtRk/0qZ+m/PvnIiIVAnNGHvBwIEDMcbgdDrp06cPkydPduf169ePHj16ADBt2jQiIiJIT0+nSZMmp5VT3r5Op5OIiIhS+0ZERLBnz54q7JVIzRcVFM2j14+jYXhD3lz9eqm89JzddGzUkS+2fs7VjToRUicUAB9z8qmieUV5RAZGAhAdFM2kG5MAKLFKmPr1lFLl5RU62XdkHy/1f4UAvwDeWvMGAKEBoe6lE3lFee56RESk+lFg7AXz588nISGB5cuXk5iYSFZWFpGRrv9MPQPg0NBQoqOjyczMLDMwLm/f0NBQcnNLr2XMzc0tNYMsIqc7fCybpKUTiQmJYdKNSaxdsNad98nGT7i/8wNc26cru3N+4fCxbABKLMu9T7B/sHvmt6ylFL/v8hCxYbGsSV/N3txMVyC+cAzB/sEkJUwhdW8KzgInwXVC3OXlFeVVca9FRKSytJTCi3r27InD4WD8+PHutPT0dPe20+kkOzubRo0alXl8efu2a9eOHTt2cOTIEXd+amoq7dq1q4JeiFwa/HxO/t1/tOgYx4rzS+UfKzrKa6v+ytSvJ1N4vJA16asB2J2zi8vrXQ5Ax0ZXs/nA5nLreOuHN5jyVRJfbP0cjCGv0IlllZBfdAw/Xz98jA+bD2zk6kadALi6USc279/k7a6KiIiXi+bbugAAD+5JREFUaMbYy8aOHUtcXJz7ArxFixaxcuVKunTpwsSJE+natWuZs8Vn27djx45MnjyZqVOnsnjxYtatW8e8efMuWL9EapomEU2575phlFgl+Bo/Zv/4Ls0i47iqYXs+3byAdg2u5PYr78CyLDbsW09K5k8AvJcyh5HXjcLPx4+UzJ/IzHUtWfJcSgHw5po32O/c536/fu86rm/WnaSEZ/H39eOLnxdTeLyQ1L2pXH3ZNSQlTOFo0TFeX/XXCzoOIiJSccby+NrQy6qs4OokLi6OmTNnkpCQ4E4bNWoUBw4cICwszH2niVWrVtGpUydmzZpF8+bNAdcDPrZt20Z8fHypu1KUte8vv/yCw+FgzZo1NG3alNdee61UneXRI6FFahY9ElpEpFLM2Xc5O80Yn6dffvnltLS//e1vgOsWbPXq1eONN94o89hT/yg5075xcXEsW7bsvNoqIiIiIuXTGmMRERERERQYi4iIiIgAWmN8ydMaY5GaRWuMRUQqxStrjDVjLCIiIiKCZoxFREREpObTjLGIiIiIiLcoMBYRERERQYGxiIiIiAigwFhEREREBFBgLCIiIiICKDAWEREREQEUGIuIiIiIAAqMRUREREQABcYiIiIiIoACYxERERERQIGxiIiIiAgAflVYtleeWS0iIiIiciFoxlhEREREBAXGIiIiIiKAAmMREREREUCBsYiIiIgIoMBYRERERARQYCwiIiIiAigwFhEREREBFBiLiIiIiAAKjEVEREREgCp88p0xZgOQX1Xl10L1gKyL3YhLiMbTuzSe3qOx9C6Np3dpPL1HY+ldgZZlXXm+hVTlI6HzLcvqXIXl1yrGmP9qPL1H4+ldGk/v0Vh6l8bTuzSe3qOx9C5jzH+9UY6WUoiIiIiIoMBYRERERASo2sD4rSosuzbSeHqXxtO7NJ7eo7H0Lo2nd2k8vUdj6V1eGU9jWZY3yhERERERqdG0lEJEREREhPMMjI0xdxpjNhpjSowx5V5ZaYy52RjzszFmuzHmTx7pzY0xa4wx24wxHxhj6pxPe2o6Y0y0MeZLezy+NMZElbFPb2NMiscr3xgz0M5LNsbs9MjreOF7UX1UZDzt/Y57jNkCj3Sdn7YKnpsdjTGr7M+EdcaYuz3ydG5S/mehR36Afa5tt8+9OI+8J+z0n40xfS9ku6ujCozlY8aYTfa5+JUxpplHXpm/87VZBcbTYYw56DFuD3rkDbM/G7YZY4Zd2JZXTxUYz5c8xnKrMSbHI0/npwdjzDvGmAP2bYDLyjfGmFfssV5njOnkkXfu56ZlWZV+AW2AK4BlQOdy9vEF0oAWQB0gFWhr5/0LuMfefgMYdT7tqekv4HngT/b2n4DnzrJ/NJANBNvvk4HBF7sf1eVV0fEEnOWk6/w8h7EELgda2duNgL1ApP2+1p+bZ/os9NhnNPCGvX0P8IG93dbePwBobpfje7H7VM3HsrfHZ+OoE2Npvy/zd762vio4ng7g1TKOjQZ22P9G2dtRF7tP1X08T9l/DPCOx3udn6XHpwfQCdhQTv5vgcWAAa4D1tjplTo3z2vG2LKszZZl/XyW3boA2y3L2mFZViHwPnCbMcYAfYCP7P1mAQPPpz2XgNtwjQNUbDwGA4styzpapa2quc51PN10fp7mrGNpWdZWy7K22duZwAEg5oK1sPor87PwlH08x/kj4Eb7XLwNeN+yrALLsnYC2+3yaquzjqVlWd94fDauBhpf4DbWJBU5N8vTF/jSsqxsy7IOA18CN1dRO2uKcx3PIcB7F6RlNZBlWd/imgQsz23AbMtlNRBpjGlIJc/NC7HG+DIg3eN9hp1WF8ixLKv4lPTarIFlWXsB7H/rn2X/ezj9l2ma/VXCS8aYgKpoZA1S0fEMNMb81xiz+sSyFHR+nuqczk1jTBdcMyVpHsm1/dws77OwzH3sc+9XXOdiRY6tTc51PB7ANaN0Qlm/87VZRcfzDvt3+CNjTJNzPLY2qfCY2Et8mgNfeyTr/Dw35Y13pc7Nsz75zhizFIgtI+spy7L+fbbjcU1tn8o6Q/ol7UzjeY7lNASuAr7wSH4C2IcrIHkLmABMqVxLawYvjWdTy7IyjTEtgK+NMeuB3DL2u6TPTy+fm/8AhlmWVWIn17pzswwV+czT52XFVHg8jDH3Ap2Bnh7Jp/3OW5aVVtbxtURFxnMh8J5lWQXGmIdwfbPRp4LH1jbnMib3AB9ZlnXcI03n57nx6ufmWQNjy7ISKtCoM8kAmni8bwxk4no+eKQxxs+eGTmRfkk703gaY/YbYxpalrXXDi4OnKGou4BPLMsq8ih7r71ZYIx5FxjvlUZXY94YT/trfyzL2mGMWQZcDcyjlp2f3hhLY0w48BnwtP2V1omya925WYbyPgvL2ifDGOMHROD6CrEix9YmFRoPY0wCrj/selqWVXAivZzf+doceJx1PC3LOuTx9m3gOY9je51y7DKvt7BmOZff13uAP3gm6Pw8Z+WNd6XOzQuxlOI/QCvjusK/Dq6TYIHlWhn9Da51sgDDgIrMQF/KFuAaBzj7eJy2JskOWE6sjx0IlHkFZy1y1vE0xkSd+FrfGFMPuB7YpPPzNBUZyzrAJ7jWen14Sp7OzXI+C0/Zx3OcBwNf2+fiAuAe47prRXOgFfDDBWp3dXTWsTTGXA28CQywLOuAR3qZv/MXrOXVU0XGs6HH2wHAZnv7C+Ame1yjgJso/U1mbVSR33WMMVfguihslUeazs9ztwC4z747xXXAr/ZkTOXOzfO8UnAQroi8ANgPfGGnNwIWnXLF4FZcf/E85ZHeAteH+3bgQyDgfNpT01+41hJ+BWyz/4220zsDMz32iwP2AD6nHP81sB5X0PFPIPRi96m6jyfwG3vMUu1/H/A4XufnuY3lvUARkOLx6mjn6dy0yv4sxLWkZIC9HWifa9vtc6+Fx7FP2cf9DNxysftysV8VGMul9v9LJ87FBXZ6ub/ztflVgfGcDmy0x+0boLXHsffb5+x2YPjF7kt1eJ1tPO33ScCfTzlO5+fpY/kerrscFeGKOR8AHgIesvMN8Jo91uvxuEtaZc5NPflORERERAQ9+U5EREREBFBgLCIiIiICKDAWEREREQEUGIuIiIiIAAqMRUREREQABcYi4iXGmKeMMRvtR8amGGO6Xuw2eYMxJtQY86YxJs3u37dV1TdjTLIxZvBZ9nEYYxp5vJ9pjGnrpfpvsR9Fu9kYs8UY83+VLCfAGLPUPg/uNsbcYI9dijHmMmPMR2c5vtJ9Msb0Msb8pjLHioic9cl3IiJnY4zpBtwKdLJcj4yth+vxz1VV34knEl4IM4GdQCvLskrsx7S2qciB9gNNjHXy0dgYY3yt0o9/PVcOXPeDPvF0rAfPoyw3Y8yVwKtAP8uytthP3vt9JYu7GvC3LKujXfYbwP9ZlvWunX/G4P88+9QLcALfn0cZIlJLacZYRLyhIZBl2Y/dtSwry7Ifa2qM+cUY85wx5gf7FW+nxxhj5hlj/mO/rrfTuxhjvjfG/GT/e4Wd7jDGfGiMWQgssWcGlxtj/mWM2WqM+bMxZqhdx3pjTEv7uP7GmDV2eUuNMQ3s9CRjzDvGmGXGmB3GmEdO7ZRdRldcj7gusfu2w7Ksz+z8x4wxG+zXWDstzp5xfR1YCzQxxjiNMVOMMWuAbsaYa+y2/2iM+eKUp4qdqHuSPS4bjDFv2U91GozroSpz7NnXILv9ne1jhth932CMec6jLKcxZpoxJtUYs/rEGJzicWCaZVlb7H4WW5b1un18M2PMV/a3AV8ZY5qW9zM0xtTH9RCXjnYbR+J6hP0kY8wce3w22Mf7GmP+z27zOmPMGDvds083GWNWGWPW2j//UI/zarKdvt4Y09oYE4frxv/j7LpvKPeMFREpy8V+ooleeulV819AKK6ni20FXgd6euT9wsknP90HfGpvzwW629tNgc32djjgZ28nAPPsbQeupx6deOpeLyAHV1AegOtpkJPtvEeBGfZ2FLgfZvQg8Bd7OwnXrGIAUA84hGuW07NfA4BPyunzNbieshRi938jrpnSOKAEuM5jXwu4y972t+uNsd/fDbxjbycDg+3taI/j/wH0t7eXUfrJTstwBcuNgN1ADK5vA78GBnrUf+L453EF+qf2Zy3QoZy+LgSG2dv3A/PP8jPsdeLnXEa/4oAN9vYoYJ7Hzzv6lD7VA74FQuz0CcAkj/NqjL09mpNPYEwCxl/s3wm99NKrZr60lEJEzptlWU5jzDXADUBv4ANjzJ8sy0q2d3nP49+X7O0EoK1rtQEA4caYMCACmGWMaYUroPP3qOpLy7KyPd7/x7KsvQDGmDRgiZ2+3m4HQGO7PQ1xLe/Y6XH8Z5ZrlrvAGHMAaIAr+K6I7riC5jy7/o/t/i8AdlmWtdpj3+O4AkCAK4ArgS/tvvvietzpqXobYx4HgoFoXIH3wjO051pgmWVZB+32zAF6APOBQuBTe78fgf9XwT6e0A243d7+B67gGsr/GVZUAvCGZS+LOeVnC3Ad0Bb4zq6jDrDKI/9j+98fPdonIlJpCoxFxCss17rZZcAyY8x6YBiumUJwBbicsu0DdLMs65hnOcaYvwLfWJY1yP5qfJlHdt4p1RZ4bJd4vC/h5OfbX4EXLctaYIzphWtGsazjj3P6Z+JGoIMxxsfyWCd8oqmU79R25lsn1xUbYKNlWd3KO9gYE4hr5r2zZVnpxpgkIPAM9Z2tPUWWZZ0Y97L6Ca6+XgOknqUeOPvPsAJFuHal9LlRVv6XlmUNKSf/xM+vvD6JiJwTrTEWkfNmjLnCnuE9oSOwy+P93R7/npjxWwI87FFGR3szAteyCHAtnzhfnuUNO5cDLctKA/4LTDZ2tGeMaWWMuQ3XV/wDjTHBxpgQYBCwogLF/gzEGNcFixhj/I0x7U7Z50QQnGWvqfW8WO0IUNas7BqgpzGmnjHGFxgCLK9QR11eAJ40xlxut8vHGPOYnfc9cI+9PRRYaW+X9zOsqCXAQ8Z1oR/GmOhT8lcD15uT69KDT7TvDMobHxGRs1JgLCLeEIpr+cMmY8w6XF9/J3nkB9gXnj0KjLPTHgE62xddbcJ10RS4vqafboz5Dtcyg/OVBHxojFkBZFXi+AeBWGC7PRP+NpBpWdZaXDPiP+AKSmdalvXT2QqzLKsQV6D7nDEmFdfa7N+csk+OXc96XEsh/uORnQy8ceLiO49j9gJPAN/gmvVda1nWvyvaScuy1gFjgfeMMZtx3fnixEWBjwDD7Z/t73D9HE+kl/UzrKiZuNZFr7PHIvGUNh3E9cfRe3bdq4HWZylzITBIF9+JSGWYk9+uiYh4nzHmF1xLAioTlIqIiFwwmjEWEREREUEzxiIiIiIigGaMRUREREQABcYiIiIiIoACYxERERERQIGxiIiIiAigwFhEREREBFBgLCIiIiICwP8HAD27S51LlmgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot_transition_genes(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.Detect differentially expressed genes between pairs of branches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum number of cells expressing genes: 5\n",
      "Importing precomputed scaled gene expression matrix ...\n",
      "There are not sufficient cells (should be greater than 20) between branches S2_S0 and S0_S1. fold_change is calculated\n",
      "There are not sufficient cells (should be greater than 20) between branches S2_S0 and S0_S3. fold_change is calculated\n",
      "There are not sufficient cells (should be greater than 20) between branches S4_S2 and S2_S0. fold_change is calculated\n",
      "There are not sufficient cells (should be greater than 20) between branches S2_S0 and S2_S5. fold_change is calculated\n"
     ]
    }
   ],
   "source": [
    "st.detect_de_genes(adata,root='S4',preference=['S2','S1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dict_DE_greater' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-066383103690>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_de_genes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/stream/core.py\u001b[0m in \u001b[0;36mplot_de_genes\u001b[0;34m(adata, num_genes, cutoff_zscore, cutoff_logfc, save_fig, fig_path, fig_size)\u001b[0m\n\u001b[1;32m   4324\u001b[0m             \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mdict_de_greater\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msub_edges_i\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4325\u001b[0m                 \u001b[0mval_greater\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict_de_greater\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msub_edges_i\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_genes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'logfc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m  \u001b[0;31m# the bar lengths\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4326\u001b[0;31m                 \u001b[0mpos_greater\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_DE_greater\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msub_edges_i\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_genes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m    \u001b[0;31m# the bar centers on the y axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4327\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4328\u001b[0m                 \u001b[0mval_greater\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_genes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dict_DE_greater' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.plot_de_genes(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>label_color</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>node</th>\n",
       "      <th>branch_id</th>\n",
       "      <th>branch_id_alias</th>\n",
       "      <th>branch_lam</th>\n",
       "      <th>branch_dist</th>\n",
       "      <th>S0_pseudotime</th>\n",
       "      <th>S3_pseudotime</th>\n",
       "      <th>S5_pseudotime</th>\n",
       "      <th>S7_pseudotime</th>\n",
       "      <th>S1_pseudotime</th>\n",
       "      <th>S4_pseudotime</th>\n",
       "      <th>S2_pseudotime</th>\n",
       "      <th>S6_pseudotime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AAACCTGAGAAACCAT-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>6184.560547</td>\n",
       "      <td>643</td>\n",
       "      <td>73</td>\n",
       "      <td>(84, 4)</td>\n",
       "      <td>(S6, S3)</td>\n",
       "      <td>0.100394</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.015118</td>\n",
       "      <td>0.006989</td>\n",
       "      <td>0.055921</td>\n",
       "      <td>0.018662</td>\n",
       "      <td>0.082058</td>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.023204</td>\n",
       "      <td>0.100394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AAACCTGAGAGTAATC-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>11161.797852</td>\n",
       "      <td>1204</td>\n",
       "      <td>80</td>\n",
       "      <td>(80, 20)</td>\n",
       "      <td>(S5, S2)</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.039525</td>\n",
       "      <td>0.047654</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.059326</td>\n",
       "      <td>0.106466</td>\n",
       "      <td>0.043391</td>\n",
       "      <td>0.031439</td>\n",
       "      <td>0.155037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AAACCTGAGATCCCAT-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>10935.719727</td>\n",
       "      <td>1224</td>\n",
       "      <td>8</td>\n",
       "      <td>(81, 4)</td>\n",
       "      <td>(S7, S3)</td>\n",
       "      <td>0.002994</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.008678</td>\n",
       "      <td>0.057610</td>\n",
       "      <td>0.002994</td>\n",
       "      <td>0.083748</td>\n",
       "      <td>0.036845</td>\n",
       "      <td>0.024893</td>\n",
       "      <td>0.116061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AAACCTGAGTCTCGGC-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>16270.428711</td>\n",
       "      <td>1973</td>\n",
       "      <td>21</td>\n",
       "      <td>(83, 20)</td>\n",
       "      <td>(S4, S2)</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.016857</td>\n",
       "      <td>0.024985</td>\n",
       "      <td>0.041487</td>\n",
       "      <td>0.036658</td>\n",
       "      <td>0.083797</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>0.008770</td>\n",
       "      <td>0.132368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AAACCTGCACTCGACG-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>8624.567383</td>\n",
       "      <td>897</td>\n",
       "      <td>55</td>\n",
       "      <td>(81, 4)</td>\n",
       "      <td>(S7, S3)</td>\n",
       "      <td>0.007092</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>0.012709</td>\n",
       "      <td>0.004581</td>\n",
       "      <td>0.053513</td>\n",
       "      <td>0.007092</td>\n",
       "      <td>0.079650</td>\n",
       "      <td>0.032747</td>\n",
       "      <td>0.020796</td>\n",
       "      <td>0.111964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TTTGTCAGTCCCTTGT-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>9866.656250</td>\n",
       "      <td>1029</td>\n",
       "      <td>5</td>\n",
       "      <td>(80, 20)</td>\n",
       "      <td>(S5, S2)</td>\n",
       "      <td>0.004941</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.035862</td>\n",
       "      <td>0.043991</td>\n",
       "      <td>0.004941</td>\n",
       "      <td>0.055663</td>\n",
       "      <td>0.102803</td>\n",
       "      <td>0.039727</td>\n",
       "      <td>0.027776</td>\n",
       "      <td>0.151374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TTTGTCAGTGCAGGTA-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>16886.117188</td>\n",
       "      <td>2083</td>\n",
       "      <td>55</td>\n",
       "      <td>(81, 4)</td>\n",
       "      <td>(S7, S3)</td>\n",
       "      <td>0.008320</td>\n",
       "      <td>0.003737</td>\n",
       "      <td>0.011481</td>\n",
       "      <td>0.003352</td>\n",
       "      <td>0.052284</td>\n",
       "      <td>0.008320</td>\n",
       "      <td>0.078422</td>\n",
       "      <td>0.031519</td>\n",
       "      <td>0.019567</td>\n",
       "      <td>0.110736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TTTGTCAGTTCAGTAC-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>8381.557617</td>\n",
       "      <td>844</td>\n",
       "      <td>11</td>\n",
       "      <td>(80, 20)</td>\n",
       "      <td>(S5, S2)</td>\n",
       "      <td>0.008306</td>\n",
       "      <td>0.000617</td>\n",
       "      <td>0.032497</td>\n",
       "      <td>0.040626</td>\n",
       "      <td>0.008306</td>\n",
       "      <td>0.052298</td>\n",
       "      <td>0.099438</td>\n",
       "      <td>0.036362</td>\n",
       "      <td>0.024411</td>\n",
       "      <td>0.148009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TTTGTCAGTTGCCTCT-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>11390.566406</td>\n",
       "      <td>1239</td>\n",
       "      <td>0</td>\n",
       "      <td>(80, 20)</td>\n",
       "      <td>(S5, S2)</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.000342</td>\n",
       "      <td>0.037072</td>\n",
       "      <td>0.045201</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.056873</td>\n",
       "      <td>0.104013</td>\n",
       "      <td>0.040938</td>\n",
       "      <td>0.028986</td>\n",
       "      <td>0.152584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TTTGTCATCAGTACGT-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>gray</td>\n",
       "      <td>7276.286621</td>\n",
       "      <td>748</td>\n",
       "      <td>75</td>\n",
       "      <td>(84, 4)</td>\n",
       "      <td>(S6, S3)</td>\n",
       "      <td>0.100974</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.014537</td>\n",
       "      <td>0.006409</td>\n",
       "      <td>0.055341</td>\n",
       "      <td>0.018081</td>\n",
       "      <td>0.081478</td>\n",
       "      <td>0.034575</td>\n",
       "      <td>0.022624</td>\n",
       "      <td>0.100974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4803 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      label label_color      n_counts  n_genes  node  \\\n",
       "0                                                                      \n",
       "AAACCTGAGAAACCAT-1  unknown        gray   6184.560547      643    73   \n",
       "AAACCTGAGAGTAATC-1  unknown        gray  11161.797852     1204    80   \n",
       "AAACCTGAGATCCCAT-1  unknown        gray  10935.719727     1224     8   \n",
       "AAACCTGAGTCTCGGC-1  unknown        gray  16270.428711     1973    21   \n",
       "AAACCTGCACTCGACG-1  unknown        gray   8624.567383      897    55   \n",
       "...                     ...         ...           ...      ...   ...   \n",
       "TTTGTCAGTCCCTTGT-1  unknown        gray   9866.656250     1029     5   \n",
       "TTTGTCAGTGCAGGTA-1  unknown        gray  16886.117188     2083    55   \n",
       "TTTGTCAGTTCAGTAC-1  unknown        gray   8381.557617      844    11   \n",
       "TTTGTCAGTTGCCTCT-1  unknown        gray  11390.566406     1239     0   \n",
       "TTTGTCATCAGTACGT-1  unknown        gray   7276.286621      748    75   \n",
       "\n",
       "                   branch_id branch_id_alias  branch_lam  branch_dist  \\\n",
       "0                                                                       \n",
       "AAACCTGAGAAACCAT-1   (84, 4)        (S6, S3)    0.100394     0.000541   \n",
       "AAACCTGAGAGTAATC-1  (80, 20)        (S5, S2)    0.001278     0.000649   \n",
       "AAACCTGAGATCCCAT-1   (81, 4)        (S7, S3)    0.002994     0.001087   \n",
       "AAACCTGAGTCTCGGC-1  (83, 20)        (S4, S2)    0.003181     0.001330   \n",
       "AAACCTGCACTCGACG-1   (81, 4)        (S7, S3)    0.007092     0.000821   \n",
       "...                      ...             ...         ...          ...   \n",
       "TTTGTCAGTCCCTTGT-1  (80, 20)        (S5, S2)    0.004941     0.000358   \n",
       "TTTGTCAGTGCAGGTA-1   (81, 4)        (S7, S3)    0.008320     0.003737   \n",
       "TTTGTCAGTTCAGTAC-1  (80, 20)        (S5, S2)    0.008306     0.000617   \n",
       "TTTGTCAGTTGCCTCT-1  (80, 20)        (S5, S2)    0.003731     0.000342   \n",
       "TTTGTCATCAGTACGT-1   (84, 4)        (S6, S3)    0.100974     0.000383   \n",
       "\n",
       "                    S0_pseudotime  S3_pseudotime  S5_pseudotime  \\\n",
       "0                                                                 \n",
       "AAACCTGAGAAACCAT-1       0.015118       0.006989       0.055921   \n",
       "AAACCTGAGAGTAATC-1       0.039525       0.047654       0.001278   \n",
       "AAACCTGAGATCCCAT-1       0.016807       0.008678       0.057610   \n",
       "AAACCTGAGTCTCGGC-1       0.016857       0.024985       0.041487   \n",
       "AAACCTGCACTCGACG-1       0.012709       0.004581       0.053513   \n",
       "...                           ...            ...            ...   \n",
       "TTTGTCAGTCCCTTGT-1       0.035862       0.043991       0.004941   \n",
       "TTTGTCAGTGCAGGTA-1       0.011481       0.003352       0.052284   \n",
       "TTTGTCAGTTCAGTAC-1       0.032497       0.040626       0.008306   \n",
       "TTTGTCAGTTGCCTCT-1       0.037072       0.045201       0.003731   \n",
       "TTTGTCATCAGTACGT-1       0.014537       0.006409       0.055341   \n",
       "\n",
       "                    S7_pseudotime  S1_pseudotime  S4_pseudotime  \\\n",
       "0                                                                 \n",
       "AAACCTGAGAAACCAT-1       0.018662       0.082058       0.035156   \n",
       "AAACCTGAGAGTAATC-1       0.059326       0.106466       0.043391   \n",
       "AAACCTGAGATCCCAT-1       0.002994       0.083748       0.036845   \n",
       "AAACCTGAGTCTCGGC-1       0.036658       0.083797       0.003181   \n",
       "AAACCTGCACTCGACG-1       0.007092       0.079650       0.032747   \n",
       "...                           ...            ...            ...   \n",
       "TTTGTCAGTCCCTTGT-1       0.055663       0.102803       0.039727   \n",
       "TTTGTCAGTGCAGGTA-1       0.008320       0.078422       0.031519   \n",
       "TTTGTCAGTTCAGTAC-1       0.052298       0.099438       0.036362   \n",
       "TTTGTCAGTTGCCTCT-1       0.056873       0.104013       0.040938   \n",
       "TTTGTCATCAGTACGT-1       0.018081       0.081478       0.034575   \n",
       "\n",
       "                    S2_pseudotime  S6_pseudotime  \n",
       "0                                                 \n",
       "AAACCTGAGAAACCAT-1       0.023204       0.100394  \n",
       "AAACCTGAGAGTAATC-1       0.031439       0.155037  \n",
       "AAACCTGAGATCCCAT-1       0.024893       0.116061  \n",
       "AAACCTGAGTCTCGGC-1       0.008770       0.132368  \n",
       "AAACCTGCACTCGACG-1       0.020796       0.111964  \n",
       "...                           ...            ...  \n",
       "TTTGTCAGTCCCTTGT-1       0.027776       0.151374  \n",
       "TTTGTCAGTGCAGGTA-1       0.019567       0.110736  \n",
       "TTTGTCAGTTCAGTAC-1       0.024411       0.148009  \n",
       "TTTGTCAGTTGCCTCT-1       0.028986       0.152584  \n",
       "TTTGTCATCAGTACGT-1       0.022624       0.100974  \n",
       "\n",
       "[4803 rows x 17 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additionally, STREAM can be also used to detect cell population-specific markers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum number of cells expressing genes: 5\n",
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    },
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
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      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n",
      "At least two distinctlabelare required\n"
     ]
    }
   ],
   "source": [
    "st.find_marker(adata,ident='label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zscore</th>\n",
       "      <th>H_statistic</th>\n",
       "      <th>H_pvalue</th>\n",
       "      <th>CMP</th>\n",
       "      <th>GMP</th>\n",
       "      <th>HSC</th>\n",
       "      <th>LMPP</th>\n",
       "      <th>MEP</th>\n",
       "      <th>MPP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Atpif1</th>\n",
       "      <td>1.87094</td>\n",
       "      <td>893.907</td>\n",
       "      <td>5.54241e-191</td>\n",
       "      <td>8.12348e-73</td>\n",
       "      <td>8.26153e-35</td>\n",
       "      <td>6.36718e-217</td>\n",
       "      <td>1.03423e-177</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.41084e-178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cd34</th>\n",
       "      <td>-1.85017</td>\n",
       "      <td>834.97</td>\n",
       "      <td>3.14343e-178</td>\n",
       "      <td>2.6501e-119</td>\n",
       "      <td>1.03478e-65</td>\n",
       "      <td>1.29655e-25</td>\n",
       "      <td>4.91601e-188</td>\n",
       "      <td>-1</td>\n",
       "      <td>3.61009e-174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cdk6</th>\n",
       "      <td>-1.75754</td>\n",
       "      <td>800.812</td>\n",
       "      <td>7.72125e-171</td>\n",
       "      <td>3.7432e-161</td>\n",
       "      <td>6.48728e-103</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.91453e-19</td>\n",
       "      <td>3.34392e-137</td>\n",
       "      <td>6.06012e-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Plac8</th>\n",
       "      <td>-1.58746</td>\n",
       "      <td>757.48</td>\n",
       "      <td>1.82323e-161</td>\n",
       "      <td>2.68676e-147</td>\n",
       "      <td>8.70888e-132</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.21129e-67</td>\n",
       "      <td>1.54719e-10</td>\n",
       "      <td>2.16437e-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coro1a</th>\n",
       "      <td>-2.17012</td>\n",
       "      <td>750.798</td>\n",
       "      <td>5.08357e-160</td>\n",
       "      <td>2.22162e-104</td>\n",
       "      <td>2.9121e-58</td>\n",
       "      <td>3.11579e-86</td>\n",
       "      <td>6.90204e-166</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.57623e-170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tmsb4x</th>\n",
       "      <td>-2.17607</td>\n",
       "      <td>738.786</td>\n",
       "      <td>2.01398e-157</td>\n",
       "      <td>3.64224e-160</td>\n",
       "      <td>2.45213e-145</td>\n",
       "      <td>5.24951e-72</td>\n",
       "      <td>2.48956e-87</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.65594e-106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ifitm1</th>\n",
       "      <td>-1.89313</td>\n",
       "      <td>729.175</td>\n",
       "      <td>2.41235e-155</td>\n",
       "      <td>1.30812e-42</td>\n",
       "      <td>6.45965e-22</td>\n",
       "      <td>4.64442e-161</td>\n",
       "      <td>3.9322e-93</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.06523e-146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aqp1</th>\n",
       "      <td>2.20709</td>\n",
       "      <td>728.582</td>\n",
       "      <td>3.24156e-155</td>\n",
       "      <td>2.18777e-111</td>\n",
       "      <td>4.30517e-84</td>\n",
       "      <td>1.16764e-135</td>\n",
       "      <td>7.45071e-143</td>\n",
       "      <td>-1</td>\n",
       "      <td>7.45071e-143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Klf1</th>\n",
       "      <td>2.18968</td>\n",
       "      <td>712.128</td>\n",
       "      <td>1.17195e-151</td>\n",
       "      <td>6.25301e-100</td>\n",
       "      <td>2.07091e-78</td>\n",
       "      <td>3.97736e-133</td>\n",
       "      <td>6.57341e-134</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.2088e-144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adgrl4</th>\n",
       "      <td>-1.9542</td>\n",
       "      <td>711.041</td>\n",
       "      <td>2.01288e-151</td>\n",
       "      <td>8.05673e-63</td>\n",
       "      <td>1.66437e-32</td>\n",
       "      <td>3.74757e-70</td>\n",
       "      <td>1.94448e-139</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.23581e-170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lsp1</th>\n",
       "      <td>-1.7439</td>\n",
       "      <td>707.635</td>\n",
       "      <td>1.09743e-150</td>\n",
       "      <td>2.70573e-44</td>\n",
       "      <td>2.84436e-15</td>\n",
       "      <td>3.93702e-106</td>\n",
       "      <td>5.88725e-144</td>\n",
       "      <td>-1</td>\n",
       "      <td>5.06233e-139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mfsd2b</th>\n",
       "      <td>2.07555</td>\n",
       "      <td>704.648</td>\n",
       "      <td>4.85534e-150</td>\n",
       "      <td>3.49271e-61</td>\n",
       "      <td>1.17493e-67</td>\n",
       "      <td>9.46792e-133</td>\n",
       "      <td>8.79935e-136</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.06542e-138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mt1</th>\n",
       "      <td>1.97966</td>\n",
       "      <td>700.796</td>\n",
       "      <td>3.30492e-149</td>\n",
       "      <td>1.20139e-86</td>\n",
       "      <td>1.10053e-24</td>\n",
       "      <td>2.07754e-131</td>\n",
       "      <td>9.24007e-133</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.20061e-139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ybx1</th>\n",
       "      <td>1.5379</td>\n",
       "      <td>687.853</td>\n",
       "      <td>2.07742e-146</td>\n",
       "      <td>3.8229e-36</td>\n",
       "      <td>3.8123e-09</td>\n",
       "      <td>4.6207e-140</td>\n",
       "      <td>8.0444e-102</td>\n",
       "      <td>-1</td>\n",
       "      <td>3.22754e-121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gm15915</th>\n",
       "      <td>2.18317</td>\n",
       "      <td>684.213</td>\n",
       "      <td>1.27239e-145</td>\n",
       "      <td>3.31562e-82</td>\n",
       "      <td>6.68114e-79</td>\n",
       "      <td>3.88366e-132</td>\n",
       "      <td>3.28312e-122</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.86518e-133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blvrb</th>\n",
       "      <td>2.20124</td>\n",
       "      <td>681.419</td>\n",
       "      <td>5.11106e-145</td>\n",
       "      <td>3.64751e-106</td>\n",
       "      <td>4.14671e-75</td>\n",
       "      <td>2.54914e-102</td>\n",
       "      <td>1.84144e-133</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.24529e-138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car1</th>\n",
       "      <td>2.20952</td>\n",
       "      <td>681.184</td>\n",
       "      <td>5.74739e-145</td>\n",
       "      <td>2.17501e-97</td>\n",
       "      <td>7.00294e-76</td>\n",
       "      <td>6.23016e-129</td>\n",
       "      <td>7.1968e-125</td>\n",
       "      <td>-1</td>\n",
       "      <td>5.18475e-130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flt3</th>\n",
       "      <td>1.77827</td>\n",
       "      <td>676.962</td>\n",
       "      <td>4.69962e-144</td>\n",
       "      <td>1.64417e-62</td>\n",
       "      <td>7.02012e-68</td>\n",
       "      <td>1.00292e-88</td>\n",
       "      <td>-1</td>\n",
       "      <td>3.15615e-160</td>\n",
       "      <td>2.14647e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gata1</th>\n",
       "      <td>2.13447</td>\n",
       "      <td>673.615</td>\n",
       "      <td>2.48676e-143</td>\n",
       "      <td>5.92007e-77</td>\n",
       "      <td>2.47925e-61</td>\n",
       "      <td>1.29226e-119</td>\n",
       "      <td>1.09173e-127</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.67876e-138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Limd2</th>\n",
       "      <td>-2.14911</td>\n",
       "      <td>673.321</td>\n",
       "      <td>2.87891e-143</td>\n",
       "      <td>4.55968e-65</td>\n",
       "      <td>2.70283e-54</td>\n",
       "      <td>1.08013e-105</td>\n",
       "      <td>3.59767e-136</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.16848e-140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tnfaip2</th>\n",
       "      <td>2.07229</td>\n",
       "      <td>671.904</td>\n",
       "      <td>5.82917e-143</td>\n",
       "      <td>7.73782e-85</td>\n",
       "      <td>2.44405e-41</td>\n",
       "      <td>8.2437e-99</td>\n",
       "      <td>2.5312e-141</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.96944e-138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pkm</th>\n",
       "      <td>-2.20776</td>\n",
       "      <td>671.34</td>\n",
       "      <td>7.71934e-143</td>\n",
       "      <td>9.59525e-91</td>\n",
       "      <td>2.31356e-76</td>\n",
       "      <td>6.11961e-95</td>\n",
       "      <td>2.74662e-119</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.58137e-150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tmem176b</th>\n",
       "      <td>-2.18059</td>\n",
       "      <td>667.116</td>\n",
       "      <td>6.31795e-142</td>\n",
       "      <td>4.97506e-85</td>\n",
       "      <td>2.35613e-35</td>\n",
       "      <td>2.88038e-136</td>\n",
       "      <td>6.57604e-105</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.86828e-136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tspo2</th>\n",
       "      <td>2.21442</td>\n",
       "      <td>661.967</td>\n",
       "      <td>8.19867e-141</td>\n",
       "      <td>1.55493e-98</td>\n",
       "      <td>2.48546e-66</td>\n",
       "      <td>1.43097e-119</td>\n",
       "      <td>6.17751e-121</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.49506e-129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mpl</th>\n",
       "      <td>1.54315</td>\n",
       "      <td>649.49</td>\n",
       "      <td>4.08153e-138</td>\n",
       "      <td>2.28882e-88</td>\n",
       "      <td>1.52198e-68</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.57858e-29</td>\n",
       "      <td>1.26581e-128</td>\n",
       "      <td>3.96908e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atp1b2</th>\n",
       "      <td>2.22192</td>\n",
       "      <td>648.831</td>\n",
       "      <td>5.66568e-138</td>\n",
       "      <td>8.39175e-106</td>\n",
       "      <td>1.42065e-67</td>\n",
       "      <td>2.62019e-114</td>\n",
       "      <td>4.95351e-116</td>\n",
       "      <td>-1</td>\n",
       "      <td>7.79915e-122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ces2g</th>\n",
       "      <td>2.20995</td>\n",
       "      <td>648.437</td>\n",
       "      <td>6.89049e-138</td>\n",
       "      <td>4.8454e-110</td>\n",
       "      <td>1.2726e-72</td>\n",
       "      <td>1.51542e-94</td>\n",
       "      <td>2.90931e-123</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.13691e-122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lcp1</th>\n",
       "      <td>-2.1392</td>\n",
       "      <td>644.047</td>\n",
       "      <td>6.12535e-137</td>\n",
       "      <td>1.01256e-74</td>\n",
       "      <td>6.46724e-65</td>\n",
       "      <td>1.69214e-80</td>\n",
       "      <td>2.92822e-124</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.86934e-140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Npm1</th>\n",
       "      <td>1.88537</td>\n",
       "      <td>642.612</td>\n",
       "      <td>1.25123e-136</td>\n",
       "      <td>3.00793e-45</td>\n",
       "      <td>1.04225e-22</td>\n",
       "      <td>3.9088e-136</td>\n",
       "      <td>3.64221e-94</td>\n",
       "      <td>-1</td>\n",
       "      <td>7.21324e-119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gpr171</th>\n",
       "      <td>-1.82918</td>\n",
       "      <td>641.945</td>\n",
       "      <td>1.7438e-136</td>\n",
       "      <td>4.78178e-53</td>\n",
       "      <td>4.89084e-20</td>\n",
       "      <td>1.07338e-65</td>\n",
       "      <td>1.40408e-139</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.24286e-127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marveld1</th>\n",
       "      <td>1.61038</td>\n",
       "      <td>30.719</td>\n",
       "      <td>1.06433e-05</td>\n",
       "      <td>0.00665474</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00219729</td>\n",
       "      <td>0.00867067</td>\n",
       "      <td>1.10627e-06</td>\n",
       "      <td>0.00219729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ppm1f</th>\n",
       "      <td>1.96573</td>\n",
       "      <td>30.6697</td>\n",
       "      <td>1.08843e-05</td>\n",
       "      <td>0.00240994</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.07138e-06</td>\n",
       "      <td>0.000332782</td>\n",
       "      <td>0.00240994</td>\n",
       "      <td>0.000513235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slc16a2</th>\n",
       "      <td>2.09362</td>\n",
       "      <td>30.2338</td>\n",
       "      <td>1.32653e-05</td>\n",
       "      <td>6.57205e-06</td>\n",
       "      <td>0.000857278</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00167101</td>\n",
       "      <td>0.000857278</td>\n",
       "      <td>0.000140256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accs</th>\n",
       "      <td>-2.09757</td>\n",
       "      <td>29.602</td>\n",
       "      <td>1.76617e-05</td>\n",
       "      <td>6.11347e-05</td>\n",
       "      <td>0.000561536</td>\n",
       "      <td>0.000561536</td>\n",
       "      <td>0.00450926</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00157793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tmem43</th>\n",
       "      <td>1.96018</td>\n",
       "      <td>29.5609</td>\n",
       "      <td>1.79937e-05</td>\n",
       "      <td>0.00995874</td>\n",
       "      <td>-1</td>\n",
       "      <td>3.87118e-06</td>\n",
       "      <td>0.00593704</td>\n",
       "      <td>0.000295888</td>\n",
       "      <td>0.000295888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ifi205</th>\n",
       "      <td>-1.94707</td>\n",
       "      <td>28.7131</td>\n",
       "      <td>2.63941e-05</td>\n",
       "      <td>0.000872277</td>\n",
       "      <td>0.000872277</td>\n",
       "      <td>0.000237538</td>\n",
       "      <td>0.00123089</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000431497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sema4a</th>\n",
       "      <td>2.15071</td>\n",
       "      <td>28.4296</td>\n",
       "      <td>2.99943e-05</td>\n",
       "      <td>8.96186e-05</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000258612</td>\n",
       "      <td>0.00024821</td>\n",
       "      <td>7.36076e-06</td>\n",
       "      <td>7.36076e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neu1</th>\n",
       "      <td>2.12997</td>\n",
       "      <td>28.385</td>\n",
       "      <td>3.06043e-05</td>\n",
       "      <td>7.15087e-06</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00206678</td>\n",
       "      <td>8.81159e-05</td>\n",
       "      <td>0.00206678</td>\n",
       "      <td>0.000165282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cog6</th>\n",
       "      <td>2.16994</td>\n",
       "      <td>27.7024</td>\n",
       "      <td>4.16124e-05</td>\n",
       "      <td>0.000161987</td>\n",
       "      <td>0.00158515</td>\n",
       "      <td>0.00472356</td>\n",
       "      <td>0.00216673</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000161987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apol9b</th>\n",
       "      <td>2.07251</td>\n",
       "      <td>27.3802</td>\n",
       "      <td>4.80955e-05</td>\n",
       "      <td>0.000116203</td>\n",
       "      <td>0.000820155</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000904932</td>\n",
       "      <td>0.000116203</td>\n",
       "      <td>0.000270234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akt3</th>\n",
       "      <td>-2.16582</td>\n",
       "      <td>26.8159</td>\n",
       "      <td>6.1949e-05</td>\n",
       "      <td>0.000267329</td>\n",
       "      <td>0.00557244</td>\n",
       "      <td>0.000618308</td>\n",
       "      <td>0.000754988</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00222252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edc3</th>\n",
       "      <td>2.15744</td>\n",
       "      <td>26.4564</td>\n",
       "      <td>7.27693e-05</td>\n",
       "      <td>0.000701248</td>\n",
       "      <td>0.00108596</td>\n",
       "      <td>0.00975308</td>\n",
       "      <td>0.0011209</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000630321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gm37642</th>\n",
       "      <td>-2.00501</td>\n",
       "      <td>26.4458</td>\n",
       "      <td>7.31148e-05</td>\n",
       "      <td>0.000240281</td>\n",
       "      <td>0.00397276</td>\n",
       "      <td>0.00662695</td>\n",
       "      <td>0.000240281</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.005512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clec4b1</th>\n",
       "      <td>1.94138</td>\n",
       "      <td>26.3713</td>\n",
       "      <td>7.55922e-05</td>\n",
       "      <td>0.0026819</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00151177</td>\n",
       "      <td>0.000113219</td>\n",
       "      <td>2.10059e-05</td>\n",
       "      <td>0.000127644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ephx1</th>\n",
       "      <td>2.05233</td>\n",
       "      <td>26.267</td>\n",
       "      <td>7.91998e-05</td>\n",
       "      <td>0.00417336</td>\n",
       "      <td>0.00375373</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00403896</td>\n",
       "      <td>2.93945e-05</td>\n",
       "      <td>0.000486166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Layn</th>\n",
       "      <td>-2.06053</td>\n",
       "      <td>25.8574</td>\n",
       "      <td>9.51024e-05</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>0.00319857</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>0.000378128</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000378128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lcn2</th>\n",
       "      <td>2.18423</td>\n",
       "      <td>25.7291</td>\n",
       "      <td>0.000100704</td>\n",
       "      <td>0.0023335</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000278421</td>\n",
       "      <td>5.91934e-05</td>\n",
       "      <td>5.91934e-05</td>\n",
       "      <td>0.00690943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>2.15251</td>\n",
       "      <td>24.7676</td>\n",
       "      <td>0.000154491</td>\n",
       "      <td>0.0018923</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00179808</td>\n",
       "      <td>0.000921284</td>\n",
       "      <td>1.84765e-05</td>\n",
       "      <td>0.00539301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mfsd8</th>\n",
       "      <td>2.1751</td>\n",
       "      <td>24.426</td>\n",
       "      <td>0.000179776</td>\n",
       "      <td>0.00054544</td>\n",
       "      <td>0.00333432</td>\n",
       "      <td>0.00202163</td>\n",
       "      <td>0.00333432</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00333432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enpp1</th>\n",
       "      <td>2.17937</td>\n",
       "      <td>23.3113</td>\n",
       "      <td>0.000294309</td>\n",
       "      <td>0.00309364</td>\n",
       "      <td>0.00539995</td>\n",
       "      <td>0.00539995</td>\n",
       "      <td>0.00148514</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0012082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gm10419</th>\n",
       "      <td>2.18959</td>\n",
       "      <td>23.1433</td>\n",
       "      <td>0.000316926</td>\n",
       "      <td>0.0033752</td>\n",
       "      <td>0.00367452</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00151288</td>\n",
       "      <td>0.000267068</td>\n",
       "      <td>0.00151288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sh3tc1</th>\n",
       "      <td>-2.1796</td>\n",
       "      <td>22.842</td>\n",
       "      <td>0.000361864</td>\n",
       "      <td>0.00495053</td>\n",
       "      <td>0.00495053</td>\n",
       "      <td>0.00055689</td>\n",
       "      <td>0.0065612</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00495053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nr3c2</th>\n",
       "      <td>1.96479</td>\n",
       "      <td>22.3162</td>\n",
       "      <td>0.000455863</td>\n",
       "      <td>0.00198642</td>\n",
       "      <td>0.00272998</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00832671</td>\n",
       "      <td>0.000429702</td>\n",
       "      <td>0.00317353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dip2b</th>\n",
       "      <td>-2.0885</td>\n",
       "      <td>22.2896</td>\n",
       "      <td>0.000461206</td>\n",
       "      <td>0.00232589</td>\n",
       "      <td>0.00141307</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00141307</td>\n",
       "      <td>0.00264283</td>\n",
       "      <td>0.00389271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mettl24</th>\n",
       "      <td>2.1774</td>\n",
       "      <td>21.3489</td>\n",
       "      <td>0.00069586</td>\n",
       "      <td>0.00336722</td>\n",
       "      <td>0.0017597</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00316082</td>\n",
       "      <td>0.0017597</td>\n",
       "      <td>0.0017597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C5ar1</th>\n",
       "      <td>2.1433</td>\n",
       "      <td>20.9406</td>\n",
       "      <td>0.000831245</td>\n",
       "      <td>0.00127867</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00127867</td>\n",
       "      <td>0.000248003</td>\n",
       "      <td>0.000248003</td>\n",
       "      <td>0.000928929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wfdc21</th>\n",
       "      <td>2.2149</td>\n",
       "      <td>20.2057</td>\n",
       "      <td>0.00114338</td>\n",
       "      <td>0.00552489</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000540168</td>\n",
       "      <td>0.000540168</td>\n",
       "      <td>0.000540168</td>\n",
       "      <td>0.000650121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unc45bos</th>\n",
       "      <td>1.9773</td>\n",
       "      <td>19.9355</td>\n",
       "      <td>0.00128504</td>\n",
       "      <td>0.00447646</td>\n",
       "      <td>0.00447646</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00496762</td>\n",
       "      <td>0.00209628</td>\n",
       "      <td>0.00711764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mcemp1</th>\n",
       "      <td>2.20377</td>\n",
       "      <td>18.5485</td>\n",
       "      <td>0.00233205</td>\n",
       "      <td>0.00641954</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0012455</td>\n",
       "      <td>0.000740696</td>\n",
       "      <td>0.00592485</td>\n",
       "      <td>0.00592485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mgam</th>\n",
       "      <td>2.21962</td>\n",
       "      <td>17.9639</td>\n",
       "      <td>0.00299201</td>\n",
       "      <td>0.00229271</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.00506753</td>\n",
       "      <td>0.00118746</td>\n",
       "      <td>0.00229271</td>\n",
       "      <td>0.00106674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1649 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           zscore H_statistic      H_pvalue           CMP           GMP  \\\n",
       "Atpif1    1.87094     893.907  5.54241e-191   8.12348e-73   8.26153e-35   \n",
       "Cd34     -1.85017      834.97  3.14343e-178   2.6501e-119   1.03478e-65   \n",
       "Cdk6     -1.75754     800.812  7.72125e-171   3.7432e-161  6.48728e-103   \n",
       "Plac8    -1.58746      757.48  1.82323e-161  2.68676e-147  8.70888e-132   \n",
       "Coro1a   -2.17012     750.798  5.08357e-160  2.22162e-104    2.9121e-58   \n",
       "Tmsb4x   -2.17607     738.786  2.01398e-157  3.64224e-160  2.45213e-145   \n",
       "Ifitm1   -1.89313     729.175  2.41235e-155   1.30812e-42   6.45965e-22   \n",
       "Aqp1      2.20709     728.582  3.24156e-155  2.18777e-111   4.30517e-84   \n",
       "Klf1      2.18968     712.128  1.17195e-151  6.25301e-100   2.07091e-78   \n",
       "Adgrl4    -1.9542     711.041  2.01288e-151   8.05673e-63   1.66437e-32   \n",
       "Lsp1      -1.7439     707.635  1.09743e-150   2.70573e-44   2.84436e-15   \n",
       "Mfsd2b    2.07555     704.648  4.85534e-150   3.49271e-61   1.17493e-67   \n",
       "Mt1       1.97966     700.796  3.30492e-149   1.20139e-86   1.10053e-24   \n",
       "Ybx1       1.5379     687.853  2.07742e-146    3.8229e-36    3.8123e-09   \n",
       "Gm15915   2.18317     684.213  1.27239e-145   3.31562e-82   6.68114e-79   \n",
       "Blvrb     2.20124     681.419  5.11106e-145  3.64751e-106   4.14671e-75   \n",
       "Car1      2.20952     681.184  5.74739e-145   2.17501e-97   7.00294e-76   \n",
       "Flt3      1.77827     676.962  4.69962e-144   1.64417e-62   7.02012e-68   \n",
       "Gata1     2.13447     673.615  2.48676e-143   5.92007e-77   2.47925e-61   \n",
       "Limd2    -2.14911     673.321  2.87891e-143   4.55968e-65   2.70283e-54   \n",
       "Tnfaip2   2.07229     671.904  5.82917e-143   7.73782e-85   2.44405e-41   \n",
       "Pkm      -2.20776      671.34  7.71934e-143   9.59525e-91   2.31356e-76   \n",
       "Tmem176b -2.18059     667.116  6.31795e-142   4.97506e-85   2.35613e-35   \n",
       "Tspo2     2.21442     661.967  8.19867e-141   1.55493e-98   2.48546e-66   \n",
       "Mpl       1.54315      649.49  4.08153e-138   2.28882e-88   1.52198e-68   \n",
       "Atp1b2    2.22192     648.831  5.66568e-138  8.39175e-106   1.42065e-67   \n",
       "Ces2g     2.20995     648.437  6.89049e-138   4.8454e-110    1.2726e-72   \n",
       "Lcp1      -2.1392     644.047  6.12535e-137   1.01256e-74   6.46724e-65   \n",
       "Npm1      1.88537     642.612  1.25123e-136   3.00793e-45   1.04225e-22   \n",
       "Gpr171   -1.82918     641.945   1.7438e-136   4.78178e-53   4.89084e-20   \n",
       "...           ...         ...           ...           ...           ...   \n",
       "Marveld1  1.61038      30.719   1.06433e-05    0.00665474            -1   \n",
       "Ppm1f     1.96573     30.6697   1.08843e-05    0.00240994            -1   \n",
       "Slc16a2   2.09362     30.2338   1.32653e-05   6.57205e-06   0.000857278   \n",
       "Accs     -2.09757      29.602   1.76617e-05   6.11347e-05   0.000561536   \n",
       "Tmem43    1.96018     29.5609   1.79937e-05    0.00995874            -1   \n",
       "Ifi205   -1.94707     28.7131   2.63941e-05   0.000872277   0.000872277   \n",
       "Sema4a    2.15071     28.4296   2.99943e-05   8.96186e-05            -1   \n",
       "Neu1      2.12997      28.385   3.06043e-05   7.15087e-06            -1   \n",
       "Cog6      2.16994     27.7024   4.16124e-05   0.000161987    0.00158515   \n",
       "Apol9b    2.07251     27.3802   4.80955e-05   0.000116203   0.000820155   \n",
       "Akt3     -2.16582     26.8159    6.1949e-05   0.000267329    0.00557244   \n",
       "Edc3      2.15744     26.4564   7.27693e-05   0.000701248    0.00108596   \n",
       "Gm37642  -2.00501     26.4458   7.31148e-05   0.000240281    0.00397276   \n",
       "Clec4b1   1.94138     26.3713   7.55922e-05     0.0026819            -1   \n",
       "Ephx1     2.05233      26.267   7.91998e-05    0.00417336    0.00375373   \n",
       "Layn     -2.06053     25.8574   9.51024e-05      0.002932    0.00319857   \n",
       "Lcn2      2.18423     25.7291   0.000100704     0.0023335            -1   \n",
       "F5        2.15251     24.7676   0.000154491     0.0018923            -1   \n",
       "Mfsd8      2.1751      24.426   0.000179776    0.00054544    0.00333432   \n",
       "Enpp1     2.17937     23.3113   0.000294309    0.00309364    0.00539995   \n",
       "Gm10419   2.18959     23.1433   0.000316926     0.0033752    0.00367452   \n",
       "Sh3tc1    -2.1796      22.842   0.000361864    0.00495053    0.00495053   \n",
       "Nr3c2     1.96479     22.3162   0.000455863    0.00198642    0.00272998   \n",
       "Dip2b     -2.0885     22.2896   0.000461206    0.00232589    0.00141307   \n",
       "Mettl24    2.1774     21.3489    0.00069586    0.00336722     0.0017597   \n",
       "C5ar1      2.1433     20.9406   0.000831245    0.00127867            -1   \n",
       "Wfdc21     2.2149     20.2057    0.00114338    0.00552489            -1   \n",
       "Unc45bos   1.9773     19.9355    0.00128504    0.00447646    0.00447646   \n",
       "Mcemp1    2.20377     18.5485    0.00233205    0.00641954            -1   \n",
       "Mgam      2.21962     17.9639    0.00299201    0.00229271            -1   \n",
       "\n",
       "                   HSC          LMPP           MEP           MPP  \n",
       "Atpif1    6.36718e-217  1.03423e-177            -1  1.41084e-178  \n",
       "Cd34       1.29655e-25  4.91601e-188            -1  3.61009e-174  \n",
       "Cdk6                -1   8.91453e-19  3.34392e-137   6.06012e-21  \n",
       "Plac8               -1   2.21129e-67   1.54719e-10   2.16437e-50  \n",
       "Coro1a     3.11579e-86  6.90204e-166            -1  9.57623e-170  \n",
       "Tmsb4x     5.24951e-72   2.48956e-87            -1  2.65594e-106  \n",
       "Ifitm1    4.64442e-161    3.9322e-93            -1  2.06523e-146  \n",
       "Aqp1      1.16764e-135  7.45071e-143            -1  7.45071e-143  \n",
       "Klf1      3.97736e-133  6.57341e-134            -1   1.2088e-144  \n",
       "Adgrl4     3.74757e-70  1.94448e-139            -1  1.23581e-170  \n",
       "Lsp1      3.93702e-106  5.88725e-144            -1  5.06233e-139  \n",
       "Mfsd2b    9.46792e-133  8.79935e-136            -1  1.06542e-138  \n",
       "Mt1       2.07754e-131  9.24007e-133            -1  4.20061e-139  \n",
       "Ybx1       4.6207e-140   8.0444e-102            -1  3.22754e-121  \n",
       "Gm15915   3.88366e-132  3.28312e-122            -1  1.86518e-133  \n",
       "Blvrb     2.54914e-102  1.84144e-133            -1  1.24529e-138  \n",
       "Car1      6.23016e-129   7.1968e-125            -1  5.18475e-130  \n",
       "Flt3       1.00292e-88            -1  3.15615e-160   2.14647e-22  \n",
       "Gata1     1.29226e-119  1.09173e-127            -1  1.67876e-138  \n",
       "Limd2     1.08013e-105  3.59767e-136            -1  2.16848e-140  \n",
       "Tnfaip2     8.2437e-99   2.5312e-141            -1  1.96944e-138  \n",
       "Pkm        6.11961e-95  2.74662e-119            -1  1.58137e-150  \n",
       "Tmem176b  2.88038e-136  6.57604e-105            -1  1.86828e-136  \n",
       "Tspo2     1.43097e-119  6.17751e-121            -1  4.49506e-129  \n",
       "Mpl                 -1   1.57858e-29  1.26581e-128   3.96908e-09  \n",
       "Atp1b2    2.62019e-114  4.95351e-116            -1  7.79915e-122  \n",
       "Ces2g      1.51542e-94  2.90931e-123            -1  1.13691e-122  \n",
       "Lcp1       1.69214e-80  2.92822e-124            -1  2.86934e-140  \n",
       "Npm1       3.9088e-136   3.64221e-94            -1  7.21324e-119  \n",
       "Gpr171     1.07338e-65  1.40408e-139            -1  1.24286e-127  \n",
       "...                ...           ...           ...           ...  \n",
       "Marveld1    0.00219729    0.00867067   1.10627e-06    0.00219729  \n",
       "Ppm1f      1.07138e-06   0.000332782    0.00240994   0.000513235  \n",
       "Slc16a2             -1    0.00167101   0.000857278   0.000140256  \n",
       "Accs       0.000561536    0.00450926            -1    0.00157793  \n",
       "Tmem43     3.87118e-06    0.00593704   0.000295888   0.000295888  \n",
       "Ifi205     0.000237538    0.00123089            -1   0.000431497  \n",
       "Sema4a     0.000258612    0.00024821   7.36076e-06   7.36076e-06  \n",
       "Neu1        0.00206678   8.81159e-05    0.00206678   0.000165282  \n",
       "Cog6        0.00472356    0.00216673            -1   0.000161987  \n",
       "Apol9b              -1   0.000904932   0.000116203   0.000270234  \n",
       "Akt3       0.000618308   0.000754988            -1    0.00222252  \n",
       "Edc3        0.00975308     0.0011209            -1   0.000630321  \n",
       "Gm37642     0.00662695   0.000240281            -1      0.005512  \n",
       "Clec4b1     0.00151177   0.000113219   2.10059e-05   0.000127644  \n",
       "Ephx1               -1    0.00403896   2.93945e-05   0.000486166  \n",
       "Layn          0.002932   0.000378128            -1   0.000378128  \n",
       "Lcn2       0.000278421   5.91934e-05   5.91934e-05    0.00690943  \n",
       "F5          0.00179808   0.000921284   1.84765e-05    0.00539301  \n",
       "Mfsd8       0.00202163    0.00333432            -1    0.00333432  \n",
       "Enpp1       0.00539995    0.00148514            -1     0.0012082  \n",
       "Gm10419             -1    0.00151288   0.000267068    0.00151288  \n",
       "Sh3tc1      0.00055689     0.0065612            -1    0.00495053  \n",
       "Nr3c2               -1    0.00832671   0.000429702    0.00317353  \n",
       "Dip2b               -1    0.00141307    0.00264283    0.00389271  \n",
       "Mettl24             -1    0.00316082     0.0017597     0.0017597  \n",
       "C5ar1       0.00127867   0.000248003   0.000248003   0.000928929  \n",
       "Wfdc21     0.000540168   0.000540168   0.000540168   0.000650121  \n",
       "Unc45bos            -1    0.00496762    0.00209628    0.00711764  \n",
       "Mcemp1       0.0012455   0.000740696    0.00592485    0.00592485  \n",
       "Mgam        0.00506753    0.00118746    0.00229271    0.00106674  \n",
       "\n",
       "[1649 rows x 9 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.uns['markers_label_all']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "st.write(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save web report (Optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the analysis result and interactively visualize it on STREAM website (http://stream.pinellolab.org/) or local STREAM_web docker image (https://github.com/pinellolab/STREAM_web)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**check parameters**  \n",
    "`st.save_web_report?`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Please run st.detect_leaf_genes(adata) before saving web report",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-7a7187642cce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m                 \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'kkarri_chow_1_test_Steram1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                 \u001b[0mdescription\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'This scRNA-seq dataset contains ~4000 cell from chow dataset with ~9000 genes coming from NASH paper.'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                 starting_node='S4',command_used='1.STREAM_scRNA-seq_kk_chow.ipynb')\n\u001b[0m",
      "\u001b[0;32m/restricted/projectnb/waxmanlab/environment/.conda/envs/myenv/lib/python3.6/site-packages/stream/core.py\u001b[0m in \u001b[0;36msave_web_report\u001b[0;34m(adata, n_genes, file_name, preference, title, description, starting_node, command_used, **kwargs)\u001b[0m\n\u001b[1;32m   4682\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Please run st.plot_flat_tree(adata) before saving web report'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4683\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'leaf_genes'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0madata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muns_keys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4684\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Please run st.detect_leaf_genes(adata) before saving web report'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4685\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'transition_genes'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0madata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muns_keys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4686\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Please run st.detect_transistion_genes(adata) before saving web report'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Please run st.detect_leaf_genes(adata) before saving web report"
     ]
    }
   ],
   "source": [
    "st.save_web_report(adata,fig_size=(8,8),n_genes=5,preference=['S2','S1'],factor_min_win=1.2,\n",
    "                title='kkarri_chow_1_test_Steram1',\n",
    "                description='This scRNA-seq dataset contains ~4000 cell from chow dataset with ~9000 genes coming from NASH paper.',\n",
    "                starting_node='S4',command_used='1.STREAM_scRNA-seq_kk_chow.ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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