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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_matrix = torch.full((8, 15), 0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 15])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500,\n",
       "         0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500],\n",
       "        [0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500,\n",
       "         0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500],\n",
       "        [0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500,\n",
       "         0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500],\n",
       "        [0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500,\n",
       "         0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500],\n",
       "        [0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500,\n",
       "         0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500],\n",
       "        [0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500,\n",
       "         0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500],\n",
       "        [0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500,\n",
       "         0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500],\n",
       "        [0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500,\n",
       "         0.1500, 0.1500, 0.1500, 0.1500, 0.1500, 0.1500]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability_matrix.masked_fill_(torch.tensor(0, dtype=torch.bool), value=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "masked_indices = torch.bernoulli()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}