1.11/name_inference.html



<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta name="robots" content="noindex">
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  
  <title>Named Tensors operator coverage &mdash; PyTorch 1.11.0 documentation</title>
  

    <link rel="canonical" href="https://pytorch.org/docs/stable/name_inference.html"/>
  

  <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
  <!-- <link rel="stylesheet" href="_static/pygments.css" type="text/css" /> -->
  <link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css" type="text/css" />
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css" type="text/css" />
  <link rel="stylesheet" href="_static/katex-math.css" type="text/css" />
  <link rel="stylesheet" href="_static/css/jit.css" type="text/css" />
    <link rel="index" title="Index" href="genindex.html" />
    <link rel="search" title="Search" href="search.html" />
    <link rel="next" title="torch.__config__" href="__config__.html" />
    <link rel="prev" title="Named Tensors" href="named_tensor.html" />


  <!-- Google Analytics -->
  
    <script async src="https://www.googletagmanager.com/gtag/js?id=UA-117752657-2"></script>
    <script>
      window.dataLayer = window.dataLayer || [];
      function gtag(){dataLayer.push(arguments);}
      gtag('js', new Date());

      gtag('config', 'UA-117752657-2');
    </script>
  
  <!-- End Google Analytics -->
  

  <script src="_static/js/modernizr.min.js"></script>

  <!-- Preload the theme fonts -->

<link rel="preload" href="_static/fonts/FreightSans/freight-sans-book.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/IBMPlexMono/IBMPlexMono-Medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-medium-italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff2" as="font" type="font/woff2" crossorigin="anonymous">

<!-- Preload the katex fonts -->

<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Math-Italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size1-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size4-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size2-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size3-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Caligraphic-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
  <link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.15.2/css/all.css" integrity="sha384-vSIIfh2YWi9wW0r9iZe7RJPrKwp6bG+s9QZMoITbCckVJqGCCRhc+ccxNcdpHuYu" crossorigin="anonymous">
</head>

<div class="container-fluid header-holder tutorials-header" id="header-holder">
  <div class="container">
    <div class="header-container">
      <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>

      <div class="main-menu">
        <ul>
          <li>
            <a href="https://pytorch.org/get-started">Get Started</a>
          </li>

          <li>
            <a href="https://pytorch.org/ecosystem">Ecosystem</a>
          </li>

          <li>
            <a href="https://pytorch.org/mobile">Mobile</a>
          </li>

          <li>
            <a href="https://pytorch.org/blog/">Blog</a>
          </li>

          <li>
            <a href="https://pytorch.org/tutorials">Tutorials</a>
          </li>

          <li class="active docs-active">
            <div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
              <a class="resource-option with-down-orange-arrow">
                Docs
              </a>
              <div class="resources-dropdown-menu">
                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/docs/stable/index.html">
                  <span class="dropdown-title">PyTorch</span>
                  <p></p>
                </a>
                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/audio/stable/index.html">
                  <span class="dropdown-title">torchaudio</span>
                  <p></p>
                </a>
                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/text/stable/index.html">
                  <span class="dropdown-title">torchtext</span>
                  <p></p>
                </a>
                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/vision/stable/index.html">
                  <span class="dropdown-title">torchvision</span>
                  <p></p>
                </a>
                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/torchrec">
                  <span class="dropdown-title">TorchRec</span>
                  <p></p>
                </a>
                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/data">
                  <span class="dropdown-title">TorchData</span>
                  <p></p>
                </a>
                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/serve/">
                  <span class="dropdown-title">TorchServe</span>
                  <p></p>
                </a>
                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/xla">
                  <span class="dropdown-title">PyTorch on XLA Devices</span>
                  <p></p>
                </a>
            </div>
          </li>

          <li>
            <div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
              <a class="resource-option with-down-arrow">
                Resources
              </a>
              <div class="resources-dropdown-menu">
                <a class="nav-dropdown-item" href="https://pytorch.org/features">
                  <span class="dropdown-title">About</span>
                  <p>Learn about PyTorch’s features and capabilities</p>
                </a>
                <a class="nav-dropdown-item" href="https://pytorch.org/#community-module">
                  <span class="dropdown-title">Community</span>
                  <p>Join the PyTorch developer community to contribute, learn, and get your questions answered.</p>
                </a>
                <a class="nav-dropdown-item" href="https://pytorch.org/resources">
                  <span class="dropdown-title">Developer Resources</span>
                  <p>Find resources and get questions answered</p>
                </a>
                <a class="nav-dropdown-item" href="https://discuss.pytorch.org/" target="_blank">
                  <span class="dropdown-title">Forums</span>
                  <p>A place to discuss PyTorch code, issues, install, research</p>
                </a>
                <a class="nav-dropdown-item" href="https://pytorch.org/hub">
                  <span class="dropdown-title">Models (Beta)</span>
                  <p>Discover, publish, and reuse pre-trained models</p>
                </a>
              </div>
            </div>
          </li>

          <li>
            <a href="https://github.com/pytorch/pytorch">GitHub</a>
          </li>
        </ul>
      </div>

      <a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
    </div>
  </div>
</div>

<body class="pytorch-body">

   
    <div class="table-of-contents-link-wrapper">
      <span>Table of Contents</span>
      <a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
    </div>

    <nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
      <div class="pytorch-side-scroll">
        <div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
          <div class="pytorch-left-menu-search">
            
    <div class="version">
      <a href='https://pytorch.org/docs/versions.html'>1.11.0 &#x25BC</a>
    </div>
    

<div role="search">
  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
    <input type="text" name="q" placeholder="Search Docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>

          </div>

          
              <p class="caption"><span class="caption-text">Notes</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cuda.html">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/ddp.html">Distributed Data Parallel</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/gradcheck.html">Gradcheck mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/hip.html">HIP (ROCm) semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/modules.html">Modules</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/numerical_accuracy.html">Numerical accuracy</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/randomness.html">Reproducibility</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/serialization.html">Serialization semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/windows.html">Windows FAQ</a></li>
</ul>
<p class="caption"><span class="caption-text">Language Bindings</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="cpp_index.html">C++</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/javadoc/">Javadoc</a></li>
<li class="toctree-l1"><a class="reference internal" href="deploy.html">torch::deploy</a></li>
</ul>
<p class="caption"><span class="caption-text">Python API</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="torch.html">torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.html">torch.nn</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.functional.html">torch.nn.functional</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensors.html">torch.Tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensor_attributes.html">Tensor Attributes</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensor_view.html">Tensor Views</a></li>
<li class="toctree-l1"><a class="reference internal" href="autograd.html">torch.autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="cuda.html">torch.cuda</a></li>
<li class="toctree-l1"><a class="reference internal" href="amp.html">torch.cuda.amp</a></li>
<li class="toctree-l1"><a class="reference internal" href="backends.html">torch.backends</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.algorithms.join.html">torch.distributed.algorithms.join</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.elastic.html">torch.distributed.elastic</a></li>
<li class="toctree-l1"><a class="reference internal" href="fsdp.html">torch.distributed.fsdp</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.optim.html">torch.distributed.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributions.html">torch.distributions</a></li>
<li class="toctree-l1"><a class="reference internal" href="fft.html">torch.fft</a></li>
<li class="toctree-l1"><a class="reference internal" href="futures.html">torch.futures</a></li>
<li class="toctree-l1"><a class="reference internal" href="fx.html">torch.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="hub.html">torch.hub</a></li>
<li class="toctree-l1"><a class="reference internal" href="jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="linalg.html">torch.linalg</a></li>
<li class="toctree-l1"><a class="reference internal" href="monitor.html">torch.monitor</a></li>
<li class="toctree-l1"><a class="reference internal" href="special.html">torch.special</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch.overrides.html">torch.overrides</a></li>
<li class="toctree-l1"><a class="reference internal" href="package.html">torch.package</a></li>
<li class="toctree-l1"><a class="reference internal" href="profiler.html">torch.profiler</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.init.html">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="onnx.html">torch.onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="optim.html">torch.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="complex_numbers.html">Complex Numbers</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_comm_hooks.html">DDP Communication Hooks</a></li>
<li class="toctree-l1"><a class="reference internal" href="pipeline.html">Pipeline Parallelism</a></li>
<li class="toctree-l1"><a class="reference internal" href="quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc.html">Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="random.html">torch.random</a></li>
<li class="toctree-l1"><a class="reference internal" href="sparse.html">torch.sparse</a></li>
<li class="toctree-l1"><a class="reference internal" href="storage.html">torch.Storage</a></li>
<li class="toctree-l1"><a class="reference internal" href="testing.html">torch.testing</a></li>
<li class="toctree-l1"><a class="reference internal" href="benchmark_utils.html">torch.utils.benchmark</a></li>
<li class="toctree-l1"><a class="reference internal" href="bottleneck.html">torch.utils.bottleneck</a></li>
<li class="toctree-l1"><a class="reference internal" href="checkpoint.html">torch.utils.checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">torch.utils.cpp_extension</a></li>
<li class="toctree-l1"><a class="reference internal" href="data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="dlpack.html">torch.utils.dlpack</a></li>
<li class="toctree-l1"><a class="reference internal" href="mobile_optimizer.html">torch.utils.mobile_optimizer</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_zoo.html">torch.utils.model_zoo</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard.html">torch.utils.tensorboard</a></li>
<li class="toctree-l1"><a class="reference internal" href="type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="named_tensor.html">Named Tensors</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Named Tensors operator coverage</a></li>
<li class="toctree-l1"><a class="reference internal" href="__config__.html">torch.__config__</a></li>
</ul>
<p class="caption"><span class="caption-text">Libraries</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/audio/stable">torchaudio</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/text/stable">torchtext</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/vision/stable">torchvision</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/serve">TorchServe</a></li>
<li class="toctree-l1"><a class="reference external" href="http://pytorch.org/xla/">PyTorch on XLA Devices</a></li>
</ul>
<p class="caption"><span class="caption-text">Community</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="community/contribution_guide.html">PyTorch Contribution Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/governance.html">PyTorch Governance</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/persons_of_interest.html">PyTorch Governance | Persons of Interest</a></li>
</ul>

            
        </div>
      </div>
    </nav>

    <div class="pytorch-container">
      <div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
        <div class="pytorch-breadcrumbs-wrapper">
          

<div role="navigation" aria-label="breadcrumbs navigation">

  <ul class="pytorch-breadcrumbs">
    
      <li>
        <a href="index.html">
          
            Docs
          
        </a> &gt;
      </li>

        
      <li>Named Tensors operator coverage</li>
    
    
      <li class="pytorch-breadcrumbs-aside">
        
            
            <a href="_sources/name_inference.rst.txt" rel="nofollow"><img src="_static/images/view-page-source-icon.svg"></a>
          
        
      </li>
    
  </ul>

  
</div>
        </div>

        <div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
          Shortcuts
        </div>
      </div>

      <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
        <div class="pytorch-content-left">

        
          <div class="rst-content">
          
            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
              
  <div class="section" id="named-tensors-operator-coverage">
<span id="name-inference-reference-doc"></span><h1>Named Tensors operator coverage<a class="headerlink" href="#named-tensors-operator-coverage" title="Permalink to this headline">¶</a></h1>
<p>Please read <a class="reference internal" href="named_tensor.html#named-tensors-doc"><span class="std std-ref">Named Tensors</span></a> first for an introduction to named tensors.</p>
<p>This document is a reference for <em>name inference</em>, a process that defines how
named tensors:</p>
<ol class="arabic simple">
<li><p>use names to provide additional automatic runtime correctness checks</p></li>
<li><p>propagate names from input tensors to output tensors</p></li>
</ol>
<p>Below is a list of all operations that are supported with named tensors
and their associated name inference rules.</p>
<p>If you don’t see an operation listed here, but it would help your use case, please
<a class="reference external" href="https://github.com/pytorch/pytorch/issues?q=is%3Aopen+is%3Aissue+label%3A%22module%3A+named+tensor%22">search if an issue has already been filed</a> and if not, <a class="reference external" href="https://github.com/pytorch/pytorch/issues/new/choose">file one</a>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The named tensor API is experimental and subject to change.</p>
</div>
<table class="colwidths-given docutils colwidths-auto align-default" id="id1">
<caption><span class="caption-text">Supported Operations</span><a class="headerlink" href="#id1" title="Permalink to this table">¶</a></caption>
<colgroup>
<col style="width: 50%" />
<col style="width: 50%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>API</p></th>
<th class="head"><p>Name inference rule</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.abs.html#torch.Tensor.abs" title="torch.Tensor.abs"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.abs()</span></code></a>, <a class="reference internal" href="generated/torch.abs.html#torch.abs" title="torch.abs"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.abs()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.abs_.html#torch.Tensor.abs_" title="torch.Tensor.abs_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.abs_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.acos.html#torch.Tensor.acos" title="torch.Tensor.acos"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.acos()</span></code></a>, <a class="reference internal" href="generated/torch.acos.html#torch.acos" title="torch.acos"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.acos()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.acos_.html#torch.Tensor.acos_" title="torch.Tensor.acos_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.acos_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.add.html#torch.Tensor.add" title="torch.Tensor.add"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.add()</span></code></a>, <a class="reference internal" href="generated/torch.add.html#torch.add" title="torch.add"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.add()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.add_.html#torch.Tensor.add_" title="torch.Tensor.add_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.add_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.addmm.html#torch.Tensor.addmm" title="torch.Tensor.addmm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.addmm()</span></code></a>, <a class="reference internal" href="generated/torch.addmm.html#torch.addmm" title="torch.addmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addmm()</span></code></a></p></td>
<td><p><a class="reference internal" href="#contracts-away-dims-doc"><span class="std std-ref">Contracts away dims</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.addmm_.html#torch.Tensor.addmm_" title="torch.Tensor.addmm_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.addmm_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#contracts-away-dims-doc"><span class="std std-ref">Contracts away dims</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.addmv.html#torch.Tensor.addmv" title="torch.Tensor.addmv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.addmv()</span></code></a>, <a class="reference internal" href="generated/torch.addmv.html#torch.addmv" title="torch.addmv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addmv()</span></code></a></p></td>
<td><p><a class="reference internal" href="#contracts-away-dims-doc"><span class="std std-ref">Contracts away dims</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.addmv_.html#torch.Tensor.addmv_" title="torch.Tensor.addmv_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.addmv_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#contracts-away-dims-doc"><span class="std std-ref">Contracts away dims</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="named_tensor.html#torch.Tensor.align_as" title="torch.Tensor.align_as"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.align_as()</span></code></a></p></td>
<td><p>See documentation</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="named_tensor.html#torch.Tensor.align_to" title="torch.Tensor.align_to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.align_to()</span></code></a></p></td>
<td><p>See documentation</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.all.html#torch.Tensor.all" title="torch.Tensor.all"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.all()</span></code></a>, <a class="reference internal" href="generated/torch.all.html#torch.all" title="torch.all"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.all()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.any.html#torch.Tensor.any" title="torch.Tensor.any"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.any()</span></code></a>, <a class="reference internal" href="generated/torch.any.html#torch.any" title="torch.any"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.any()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.asin.html#torch.Tensor.asin" title="torch.Tensor.asin"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.asin()</span></code></a>, <a class="reference internal" href="generated/torch.asin.html#torch.asin" title="torch.asin"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.asin()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.asin_.html#torch.Tensor.asin_" title="torch.Tensor.asin_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.asin_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.atan.html#torch.Tensor.atan" title="torch.Tensor.atan"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.atan()</span></code></a>, <a class="reference internal" href="generated/torch.atan.html#torch.atan" title="torch.atan"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atan()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.atan2.html#torch.Tensor.atan2" title="torch.Tensor.atan2"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.atan2()</span></code></a>, <a class="reference internal" href="generated/torch.atan2.html#torch.atan2" title="torch.atan2"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atan2()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.atan2_.html#torch.Tensor.atan2_" title="torch.Tensor.atan2_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.atan2_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.atan_.html#torch.Tensor.atan_" title="torch.Tensor.atan_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.atan_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bernoulli.html#torch.Tensor.bernoulli" title="torch.Tensor.bernoulli"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.bernoulli()</span></code></a>, <a class="reference internal" href="generated/torch.bernoulli.html#torch.bernoulli" title="torch.bernoulli"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bernoulli()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bernoulli_.html#torch.Tensor.bernoulli_" title="torch.Tensor.bernoulli_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.bernoulli_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bfloat16.html#torch.Tensor.bfloat16" title="torch.Tensor.bfloat16"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.bfloat16()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_not.html#torch.Tensor.bitwise_not" title="torch.Tensor.bitwise_not"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.bitwise_not()</span></code></a>, <a class="reference internal" href="generated/torch.bitwise_not.html#torch.bitwise_not" title="torch.bitwise_not"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bitwise_not()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_not_.html#torch.Tensor.bitwise_not_" title="torch.Tensor.bitwise_not_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.bitwise_not_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bmm.html#torch.Tensor.bmm" title="torch.Tensor.bmm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.bmm()</span></code></a>, <a class="reference internal" href="generated/torch.bmm.html#torch.bmm" title="torch.bmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bmm()</span></code></a></p></td>
<td><p><a class="reference internal" href="#contracts-away-dims-doc"><span class="std std-ref">Contracts away dims</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bool.html#torch.Tensor.bool" title="torch.Tensor.bool"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.bool()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.byte.html#torch.Tensor.byte" title="torch.Tensor.byte"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.byte()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.cat.html#torch.cat" title="torch.cat"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cat()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.cauchy_.html#torch.Tensor.cauchy_" title="torch.Tensor.cauchy_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.cauchy_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.ceil.html#torch.Tensor.ceil" title="torch.Tensor.ceil"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.ceil()</span></code></a>, <a class="reference internal" href="generated/torch.ceil.html#torch.ceil" title="torch.ceil"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.ceil()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.ceil_.html#torch.Tensor.ceil_" title="torch.Tensor.ceil_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.ceil_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.char.html#torch.Tensor.char" title="torch.Tensor.char"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.char()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.chunk.html#torch.Tensor.chunk" title="torch.Tensor.chunk"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.chunk()</span></code></a>, <a class="reference internal" href="generated/torch.chunk.html#torch.chunk" title="torch.chunk"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.chunk()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.clamp.html#torch.Tensor.clamp" title="torch.Tensor.clamp"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.clamp()</span></code></a>, <a class="reference internal" href="generated/torch.clamp.html#torch.clamp" title="torch.clamp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.clamp()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.clamp_.html#torch.Tensor.clamp_" title="torch.Tensor.clamp_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.clamp_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.copy_.html#torch.Tensor.copy_" title="torch.Tensor.copy_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.copy_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#out-function-semantics-doc"><span class="std std-ref">out function and in-place variants</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.cos.html#torch.Tensor.cos" title="torch.Tensor.cos"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.cos()</span></code></a>, <a class="reference internal" href="generated/torch.cos.html#torch.cos" title="torch.cos"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cos()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.cos_.html#torch.Tensor.cos_" title="torch.Tensor.cos_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.cos_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.cosh.html#torch.Tensor.cosh" title="torch.Tensor.cosh"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.cosh()</span></code></a>, <a class="reference internal" href="generated/torch.cosh.html#torch.cosh" title="torch.cosh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cosh()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.cosh_.html#torch.Tensor.cosh_" title="torch.Tensor.cosh_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.cosh_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.acosh.html#torch.Tensor.acosh" title="torch.Tensor.acosh"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.acosh()</span></code></a>, <a class="reference internal" href="generated/torch.acosh.html#torch.acosh" title="torch.acosh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.acosh()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.acosh_.html#torch.Tensor.acosh_" title="torch.Tensor.acosh_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.acosh_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.cpu.html#torch.Tensor.cpu" title="torch.Tensor.cpu"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.cpu()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.cuda.html#torch.Tensor.cuda" title="torch.Tensor.cuda"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.cuda()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.cumprod.html#torch.Tensor.cumprod" title="torch.Tensor.cumprod"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.cumprod()</span></code></a>, <a class="reference internal" href="generated/torch.cumprod.html#torch.cumprod" title="torch.cumprod"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cumprod()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.cumsum.html#torch.Tensor.cumsum" title="torch.Tensor.cumsum"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.cumsum()</span></code></a>, <a class="reference internal" href="generated/torch.cumsum.html#torch.cumsum" title="torch.cumsum"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cumsum()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.data_ptr.html#torch.Tensor.data_ptr" title="torch.Tensor.data_ptr"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.data_ptr()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.deg2rad.html#torch.Tensor.deg2rad" title="torch.Tensor.deg2rad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.deg2rad()</span></code></a>, <a class="reference internal" href="generated/torch.deg2rad.html#torch.deg2rad" title="torch.deg2rad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.deg2rad()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.deg2rad_()</span></code></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.detach.html#torch.Tensor.detach" title="torch.Tensor.detach"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.detach()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.detach()</span></code></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.detach_.html#torch.Tensor.detach_" title="torch.Tensor.detach_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.detach_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.device.html#torch.Tensor.device" title="torch.Tensor.device"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Tensor.device</span></code></a>, <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.device()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.digamma.html#torch.Tensor.digamma" title="torch.Tensor.digamma"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.digamma()</span></code></a>, <a class="reference internal" href="generated/torch.digamma.html#torch.digamma" title="torch.digamma"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.digamma()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.digamma_.html#torch.Tensor.digamma_" title="torch.Tensor.digamma_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.digamma_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.dim.html#torch.Tensor.dim" title="torch.Tensor.dim"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.dim()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.div.html#torch.Tensor.div" title="torch.Tensor.div"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.div()</span></code></a>, <a class="reference internal" href="generated/torch.div.html#torch.div" title="torch.div"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.div()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.div_.html#torch.Tensor.div_" title="torch.Tensor.div_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.div_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.dot.html#torch.Tensor.dot" title="torch.Tensor.dot"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.dot()</span></code></a>, <a class="reference internal" href="generated/torch.dot.html#torch.dot" title="torch.dot"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.dot()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.double.html#torch.Tensor.double" title="torch.Tensor.double"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.double()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.element_size.html#torch.Tensor.element_size" title="torch.Tensor.element_size"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.element_size()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.empty.html#torch.empty" title="torch.empty"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty()</span></code></a></p></td>
<td><p><a class="reference internal" href="#factory-doc"><span class="std std-ref">Factory functions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.empty_like.html#torch.empty_like" title="torch.empty_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty_like()</span></code></a></p></td>
<td><p><a class="reference internal" href="#factory-doc"><span class="std std-ref">Factory functions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.eq.html#torch.Tensor.eq" title="torch.Tensor.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.eq()</span></code></a>, <a class="reference internal" href="generated/torch.eq.html#torch.eq" title="torch.eq"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.eq()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.erf.html#torch.Tensor.erf" title="torch.Tensor.erf"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.erf()</span></code></a>, <a class="reference internal" href="generated/torch.erf.html#torch.erf" title="torch.erf"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.erf()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.erf_.html#torch.Tensor.erf_" title="torch.Tensor.erf_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.erf_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.erfc.html#torch.Tensor.erfc" title="torch.Tensor.erfc"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.erfc()</span></code></a>, <a class="reference internal" href="generated/torch.erfc.html#torch.erfc" title="torch.erfc"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.erfc()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.erfc_.html#torch.Tensor.erfc_" title="torch.Tensor.erfc_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.erfc_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.erfinv.html#torch.Tensor.erfinv" title="torch.Tensor.erfinv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.erfinv()</span></code></a>, <a class="reference internal" href="generated/torch.erfinv.html#torch.erfinv" title="torch.erfinv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.erfinv()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.erfinv_.html#torch.Tensor.erfinv_" title="torch.Tensor.erfinv_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.erfinv_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.exp.html#torch.Tensor.exp" title="torch.Tensor.exp"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.exp()</span></code></a>, <a class="reference internal" href="generated/torch.exp.html#torch.exp" title="torch.exp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.exp()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.exp_.html#torch.Tensor.exp_" title="torch.Tensor.exp_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.exp_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.expand.html#torch.Tensor.expand" title="torch.Tensor.expand"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.expand()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.expm1.html#torch.Tensor.expm1" title="torch.Tensor.expm1"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.expm1()</span></code></a>, <a class="reference internal" href="generated/torch.expm1.html#torch.expm1" title="torch.expm1"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.expm1()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.expm1_.html#torch.Tensor.expm1_" title="torch.Tensor.expm1_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.expm1_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.exponential_.html#torch.Tensor.exponential_" title="torch.Tensor.exponential_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.exponential_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.fill_.html#torch.Tensor.fill_" title="torch.Tensor.fill_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.fill_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.flatten.html#torch.Tensor.flatten" title="torch.Tensor.flatten"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.flatten()</span></code></a>, <a class="reference internal" href="generated/torch.flatten.html#torch.flatten" title="torch.flatten"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.flatten()</span></code></a></p></td>
<td><p>See documentation</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.float.html#torch.Tensor.float" title="torch.Tensor.float"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.float()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.floor.html#torch.Tensor.floor" title="torch.Tensor.floor"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.floor()</span></code></a>, <a class="reference internal" href="generated/torch.floor.html#torch.floor" title="torch.floor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.floor()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.floor_.html#torch.Tensor.floor_" title="torch.Tensor.floor_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.floor_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.frac.html#torch.Tensor.frac" title="torch.Tensor.frac"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.frac()</span></code></a>, <a class="reference internal" href="generated/torch.frac.html#torch.frac" title="torch.frac"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.frac()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.frac_.html#torch.Tensor.frac_" title="torch.Tensor.frac_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.frac_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.ge.html#torch.Tensor.ge" title="torch.Tensor.ge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.ge()</span></code></a>, <a class="reference internal" href="generated/torch.ge.html#torch.ge" title="torch.ge"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.ge()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.get_device.html#torch.Tensor.get_device" title="torch.Tensor.get_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.get_device()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.get_device()</span></code></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.grad.html#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Tensor.grad</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.gt.html#torch.Tensor.gt" title="torch.Tensor.gt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.gt()</span></code></a>, <a class="reference internal" href="generated/torch.gt.html#torch.gt" title="torch.gt"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.gt()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.half.html#torch.Tensor.half" title="torch.Tensor.half"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.half()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.has_names()</span></code></p></td>
<td><p>See documentation</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.index_fill.html#torch.Tensor.index_fill" title="torch.Tensor.index_fill"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.index_fill()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.index_fill()</span></code></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.index_fill_.html#torch.Tensor.index_fill_" title="torch.Tensor.index_fill_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.index_fill_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.int.html#torch.Tensor.int" title="torch.Tensor.int"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.int()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.is_contiguous.html#torch.Tensor.is_contiguous" title="torch.Tensor.is_contiguous"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.is_contiguous()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.is_cuda.html#torch.Tensor.is_cuda" title="torch.Tensor.is_cuda"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Tensor.is_cuda</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.is_floating_point.html#torch.Tensor.is_floating_point" title="torch.Tensor.is_floating_point"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.is_floating_point()</span></code></a>, <a class="reference internal" href="generated/torch.is_floating_point.html#torch.is_floating_point" title="torch.is_floating_point"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.is_floating_point()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.is_leaf.html#torch.Tensor.is_leaf" title="torch.Tensor.is_leaf"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Tensor.is_leaf</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.is_pinned.html#torch.Tensor.is_pinned" title="torch.Tensor.is_pinned"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.is_pinned()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.is_shared.html#torch.Tensor.is_shared" title="torch.Tensor.is_shared"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.is_shared()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.is_signed.html#torch.Tensor.is_signed" title="torch.Tensor.is_signed"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.is_signed()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.is_signed()</span></code></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.is_sparse.html#torch.Tensor.is_sparse" title="torch.Tensor.is_sparse"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Tensor.is_sparse</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">Tensor.is_sparse_csr</span></code></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.is_tensor.html#torch.is_tensor" title="torch.is_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.is_tensor()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.item.html#torch.Tensor.item" title="torch.Tensor.item"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.item()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.kthvalue.html#torch.Tensor.kthvalue" title="torch.Tensor.kthvalue"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.kthvalue()</span></code></a>, <a class="reference internal" href="generated/torch.kthvalue.html#torch.kthvalue" title="torch.kthvalue"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.kthvalue()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.le.html#torch.Tensor.le" title="torch.Tensor.le"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.le()</span></code></a>, <a class="reference internal" href="generated/torch.le.html#torch.le" title="torch.le"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.le()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.log.html#torch.Tensor.log" title="torch.Tensor.log"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.log()</span></code></a>, <a class="reference internal" href="generated/torch.log.html#torch.log" title="torch.log"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.log()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.log10.html#torch.Tensor.log10" title="torch.Tensor.log10"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.log10()</span></code></a>, <a class="reference internal" href="generated/torch.log10.html#torch.log10" title="torch.log10"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.log10()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.log10_.html#torch.Tensor.log10_" title="torch.Tensor.log10_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.log10_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.log1p.html#torch.Tensor.log1p" title="torch.Tensor.log1p"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.log1p()</span></code></a>, <a class="reference internal" href="generated/torch.log1p.html#torch.log1p" title="torch.log1p"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.log1p()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.log1p_.html#torch.Tensor.log1p_" title="torch.Tensor.log1p_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.log1p_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.log2.html#torch.Tensor.log2" title="torch.Tensor.log2"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.log2()</span></code></a>, <a class="reference internal" href="generated/torch.log2.html#torch.log2" title="torch.log2"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.log2()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.log2_.html#torch.Tensor.log2_" title="torch.Tensor.log2_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.log2_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.log_.html#torch.Tensor.log_" title="torch.Tensor.log_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.log_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.log_normal_.html#torch.Tensor.log_normal_" title="torch.Tensor.log_normal_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.log_normal_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.logical_not.html#torch.Tensor.logical_not" title="torch.Tensor.logical_not"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.logical_not()</span></code></a>, <a class="reference internal" href="generated/torch.logical_not.html#torch.logical_not" title="torch.logical_not"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.logical_not()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.logical_not_.html#torch.Tensor.logical_not_" title="torch.Tensor.logical_not_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.logical_not_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.logsumexp.html#torch.Tensor.logsumexp" title="torch.Tensor.logsumexp"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.logsumexp()</span></code></a>, <a class="reference internal" href="generated/torch.logsumexp.html#torch.logsumexp" title="torch.logsumexp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.logsumexp()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.long.html#torch.Tensor.long" title="torch.Tensor.long"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.long()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.lt.html#torch.Tensor.lt" title="torch.Tensor.lt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.lt()</span></code></a>, <a class="reference internal" href="generated/torch.lt.html#torch.lt" title="torch.lt"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.lt()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.manual_seed()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.masked_fill.html#torch.Tensor.masked_fill" title="torch.Tensor.masked_fill"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.masked_fill()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.masked_fill()</span></code></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.masked_fill_.html#torch.Tensor.masked_fill_" title="torch.Tensor.masked_fill_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.masked_fill_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.masked_select.html#torch.Tensor.masked_select" title="torch.Tensor.masked_select"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.masked_select()</span></code></a>, <a class="reference internal" href="generated/torch.masked_select.html#torch.masked_select" title="torch.masked_select"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.masked_select()</span></code></a></p></td>
<td><p>Aligns mask up to input and then unifies_names_from_input_tensors</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.matmul.html#torch.Tensor.matmul" title="torch.Tensor.matmul"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.matmul()</span></code></a>, <a class="reference internal" href="generated/torch.matmul.html#torch.matmul" title="torch.matmul"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.matmul()</span></code></a></p></td>
<td><p><a class="reference internal" href="#contracts-away-dims-doc"><span class="std std-ref">Contracts away dims</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.mean.html#torch.Tensor.mean" title="torch.Tensor.mean"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.mean()</span></code></a>, <a class="reference internal" href="generated/torch.mean.html#torch.mean" title="torch.mean"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mean()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.median.html#torch.Tensor.median" title="torch.Tensor.median"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.median()</span></code></a>, <a class="reference internal" href="generated/torch.median.html#torch.median" title="torch.median"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.median()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.nanmedian.html#torch.Tensor.nanmedian" title="torch.Tensor.nanmedian"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.nanmedian()</span></code></a>, <a class="reference internal" href="generated/torch.nanmedian.html#torch.nanmedian" title="torch.nanmedian"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nanmedian()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.mm.html#torch.Tensor.mm" title="torch.Tensor.mm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.mm()</span></code></a>, <a class="reference internal" href="generated/torch.mm.html#torch.mm" title="torch.mm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mm()</span></code></a></p></td>
<td><p><a class="reference internal" href="#contracts-away-dims-doc"><span class="std std-ref">Contracts away dims</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.mode.html#torch.Tensor.mode" title="torch.Tensor.mode"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.mode()</span></code></a>, <a class="reference internal" href="generated/torch.mode.html#torch.mode" title="torch.mode"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mode()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.mul.html#torch.Tensor.mul" title="torch.Tensor.mul"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.mul()</span></code></a>, <a class="reference internal" href="generated/torch.mul.html#torch.mul" title="torch.mul"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mul()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.mul_.html#torch.Tensor.mul_" title="torch.Tensor.mul_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.mul_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.mv.html#torch.Tensor.mv" title="torch.Tensor.mv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.mv()</span></code></a>, <a class="reference internal" href="generated/torch.mv.html#torch.mv" title="torch.mv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mv()</span></code></a></p></td>
<td><p><a class="reference internal" href="#contracts-away-dims-doc"><span class="std std-ref">Contracts away dims</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="named_tensor.html#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Tensor.names</span></code></a></p></td>
<td><p>See documentation</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.narrow.html#torch.Tensor.narrow" title="torch.Tensor.narrow"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.narrow()</span></code></a>, <a class="reference internal" href="generated/torch.narrow.html#torch.narrow" title="torch.narrow"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.narrow()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.ndim.html#torch.Tensor.ndim" title="torch.Tensor.ndim"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Tensor.ndim</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.ndimension.html#torch.Tensor.ndimension" title="torch.Tensor.ndimension"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.ndimension()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.ne.html#torch.Tensor.ne" title="torch.Tensor.ne"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.ne()</span></code></a>, <a class="reference internal" href="generated/torch.ne.html#torch.ne" title="torch.ne"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.ne()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.neg.html#torch.Tensor.neg" title="torch.Tensor.neg"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.neg()</span></code></a>, <a class="reference internal" href="generated/torch.neg.html#torch.neg" title="torch.neg"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.neg()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.neg_.html#torch.Tensor.neg_" title="torch.Tensor.neg_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.neg_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.normal.html#torch.normal" title="torch.normal"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.normal()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.normal_.html#torch.Tensor.normal_" title="torch.Tensor.normal_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.normal_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.numel.html#torch.Tensor.numel" title="torch.Tensor.numel"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.numel()</span></code></a>, <a class="reference internal" href="generated/torch.numel.html#torch.numel" title="torch.numel"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.numel()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.ones.html#torch.ones" title="torch.ones"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.ones()</span></code></a></p></td>
<td><p><a class="reference internal" href="#factory-doc"><span class="std std-ref">Factory functions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.pow.html#torch.Tensor.pow" title="torch.Tensor.pow"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.pow()</span></code></a>, <a class="reference internal" href="generated/torch.pow.html#torch.pow" title="torch.pow"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.pow()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.pow_.html#torch.Tensor.pow_" title="torch.Tensor.pow_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.pow_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.prod.html#torch.Tensor.prod" title="torch.Tensor.prod"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.prod()</span></code></a>, <a class="reference internal" href="generated/torch.prod.html#torch.prod" title="torch.prod"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.prod()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.rad2deg.html#torch.Tensor.rad2deg" title="torch.Tensor.rad2deg"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.rad2deg()</span></code></a>, <a class="reference internal" href="generated/torch.rad2deg.html#torch.rad2deg" title="torch.rad2deg"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rad2deg()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.rad2deg_()</span></code></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.rand.html#torch.rand" title="torch.rand"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rand()</span></code></a></p></td>
<td><p><a class="reference internal" href="#factory-doc"><span class="std std-ref">Factory functions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.rand.html#torch.rand" title="torch.rand"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rand()</span></code></a></p></td>
<td><p><a class="reference internal" href="#factory-doc"><span class="std std-ref">Factory functions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.randn.html#torch.randn" title="torch.randn"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn()</span></code></a></p></td>
<td><p><a class="reference internal" href="#factory-doc"><span class="std std-ref">Factory functions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.randn.html#torch.randn" title="torch.randn"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn()</span></code></a></p></td>
<td><p><a class="reference internal" href="#factory-doc"><span class="std std-ref">Factory functions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.random_.html#torch.Tensor.random_" title="torch.Tensor.random_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.random_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.reciprocal.html#torch.Tensor.reciprocal" title="torch.Tensor.reciprocal"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.reciprocal()</span></code></a>, <a class="reference internal" href="generated/torch.reciprocal.html#torch.reciprocal" title="torch.reciprocal"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.reciprocal()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.reciprocal_.html#torch.Tensor.reciprocal_" title="torch.Tensor.reciprocal_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.reciprocal_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="named_tensor.html#torch.Tensor.refine_names" title="torch.Tensor.refine_names"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.refine_names()</span></code></a></p></td>
<td><p>See documentation</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.register_hook.html#torch.Tensor.register_hook" title="torch.Tensor.register_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.register_hook()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="named_tensor.html#torch.Tensor.rename" title="torch.Tensor.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.rename()</span></code></a></p></td>
<td><p>See documentation</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="named_tensor.html#torch.Tensor.rename_" title="torch.Tensor.rename_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.rename_()</span></code></a></p></td>
<td><p>See documentation</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.requires_grad.html#torch.Tensor.requires_grad" title="torch.Tensor.requires_grad"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Tensor.requires_grad</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.requires_grad_.html#torch.Tensor.requires_grad_" title="torch.Tensor.requires_grad_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.requires_grad_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.resize_.html#torch.Tensor.resize_" title="torch.Tensor.resize_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.resize_()</span></code></a></p></td>
<td><p>Only allow resizes that do not change shape</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.resize_as_.html#torch.Tensor.resize_as_" title="torch.Tensor.resize_as_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.resize_as_()</span></code></a></p></td>
<td><p>Only allow resizes that do not change shape</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.round.html#torch.Tensor.round" title="torch.Tensor.round"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.round()</span></code></a>, <a class="reference internal" href="generated/torch.round.html#torch.round" title="torch.round"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.round()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.round_.html#torch.Tensor.round_" title="torch.Tensor.round_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.round_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.rsqrt.html#torch.Tensor.rsqrt" title="torch.Tensor.rsqrt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.rsqrt()</span></code></a>, <a class="reference internal" href="generated/torch.rsqrt.html#torch.rsqrt" title="torch.rsqrt"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rsqrt()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.rsqrt_.html#torch.Tensor.rsqrt_" title="torch.Tensor.rsqrt_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.rsqrt_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.select.html#torch.Tensor.select" title="torch.Tensor.select"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.select()</span></code></a>, <a class="reference internal" href="generated/torch.select.html#torch.select" title="torch.select"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.select()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.short.html#torch.Tensor.short" title="torch.Tensor.short"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.short()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.sigmoid.html#torch.Tensor.sigmoid" title="torch.Tensor.sigmoid"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sigmoid()</span></code></a>, <a class="reference internal" href="generated/torch.sigmoid.html#torch.sigmoid" title="torch.sigmoid"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sigmoid()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.sigmoid_.html#torch.Tensor.sigmoid_" title="torch.Tensor.sigmoid_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sigmoid_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.sign.html#torch.Tensor.sign" title="torch.Tensor.sign"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sign()</span></code></a>, <a class="reference internal" href="generated/torch.sign.html#torch.sign" title="torch.sign"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sign()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.sign_.html#torch.Tensor.sign_" title="torch.Tensor.sign_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sign_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.sgn.html#torch.Tensor.sgn" title="torch.Tensor.sgn"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sgn()</span></code></a>, <a class="reference internal" href="generated/torch.sgn.html#torch.sgn" title="torch.sgn"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sgn()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.sgn_.html#torch.Tensor.sgn_" title="torch.Tensor.sgn_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sgn_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.sin.html#torch.Tensor.sin" title="torch.Tensor.sin"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sin()</span></code></a>, <a class="reference internal" href="generated/torch.sin.html#torch.sin" title="torch.sin"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sin()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.sin_.html#torch.Tensor.sin_" title="torch.Tensor.sin_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sin_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.sinh.html#torch.Tensor.sinh" title="torch.Tensor.sinh"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sinh()</span></code></a>, <a class="reference internal" href="generated/torch.sinh.html#torch.sinh" title="torch.sinh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sinh()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.sinh_.html#torch.Tensor.sinh_" title="torch.Tensor.sinh_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sinh_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.asinh.html#torch.Tensor.asinh" title="torch.Tensor.asinh"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.asinh()</span></code></a>, <a class="reference internal" href="generated/torch.asinh.html#torch.asinh" title="torch.asinh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.asinh()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.asinh_.html#torch.Tensor.asinh_" title="torch.Tensor.asinh_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.asinh_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.size.html#torch.Tensor.size" title="torch.Tensor.size"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.size()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.split.html#torch.Tensor.split" title="torch.Tensor.split"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.split()</span></code></a>, <a class="reference internal" href="generated/torch.split.html#torch.split" title="torch.split"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.split()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.sqrt.html#torch.Tensor.sqrt" title="torch.Tensor.sqrt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sqrt()</span></code></a>, <a class="reference internal" href="generated/torch.sqrt.html#torch.sqrt" title="torch.sqrt"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sqrt()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.sqrt_.html#torch.Tensor.sqrt_" title="torch.Tensor.sqrt_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sqrt_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.squeeze.html#torch.Tensor.squeeze" title="torch.Tensor.squeeze"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.squeeze()</span></code></a>, <a class="reference internal" href="generated/torch.squeeze.html#torch.squeeze" title="torch.squeeze"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.squeeze()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.std.html#torch.Tensor.std" title="torch.Tensor.std"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.std()</span></code></a>, <a class="reference internal" href="generated/torch.std.html#torch.std" title="torch.std"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.std()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.std_mean.html#torch.std_mean" title="torch.std_mean"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.std_mean()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.stride.html#torch.Tensor.stride" title="torch.Tensor.stride"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.stride()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.sub.html#torch.Tensor.sub" title="torch.Tensor.sub"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sub()</span></code></a>, <a class="reference internal" href="generated/torch.sub.html#torch.sub" title="torch.sub"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sub()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.sub_.html#torch.Tensor.sub_" title="torch.Tensor.sub_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sub_()</span></code></a></p></td>
<td><p><a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.sum.html#torch.Tensor.sum" title="torch.Tensor.sum"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.sum()</span></code></a>, <a class="reference internal" href="generated/torch.sum.html#torch.sum" title="torch.sum"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sum()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.tan.html#torch.Tensor.tan" title="torch.Tensor.tan"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.tan()</span></code></a>, <a class="reference internal" href="generated/torch.tan.html#torch.tan" title="torch.tan"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tan()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.tan_.html#torch.Tensor.tan_" title="torch.Tensor.tan_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.tan_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.tanh.html#torch.Tensor.tanh" title="torch.Tensor.tanh"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.tanh()</span></code></a>, <a class="reference internal" href="generated/torch.tanh.html#torch.tanh" title="torch.tanh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tanh()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.tanh_.html#torch.Tensor.tanh_" title="torch.Tensor.tanh_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.tanh_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.atanh.html#torch.Tensor.atanh" title="torch.Tensor.atanh"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.atanh()</span></code></a>, <a class="reference internal" href="generated/torch.atanh.html#torch.atanh" title="torch.atanh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atanh()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.atanh_.html#torch.Tensor.atanh_" title="torch.Tensor.atanh_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.atanh_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a></p></td>
<td><p><a class="reference internal" href="#factory-doc"><span class="std std-ref">Factory functions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.to()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.topk.html#torch.Tensor.topk" title="torch.Tensor.topk"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.topk()</span></code></a>, <a class="reference internal" href="generated/torch.topk.html#torch.topk" title="torch.topk"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.topk()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.transpose.html#torch.Tensor.transpose" title="torch.Tensor.transpose"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.transpose()</span></code></a>, <a class="reference internal" href="generated/torch.transpose.html#torch.transpose" title="torch.transpose"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.transpose()</span></code></a></p></td>
<td><p><a class="reference internal" href="#permutes-dimensions-doc"><span class="std std-ref">Permutes dimensions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.trunc.html#torch.Tensor.trunc" title="torch.Tensor.trunc"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.trunc()</span></code></a>, <a class="reference internal" href="generated/torch.trunc.html#torch.trunc" title="torch.trunc"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.trunc()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.trunc_.html#torch.Tensor.trunc_" title="torch.Tensor.trunc_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.trunc_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.type.html#torch.Tensor.type" title="torch.Tensor.type"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.type()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.type_as.html#torch.Tensor.type_as" title="torch.Tensor.type_as"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.type_as()</span></code></a></p></td>
<td><p><a class="reference internal" href="#keeps-input-names-doc"><span class="std std-ref">Keeps input names</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.unbind.html#torch.Tensor.unbind" title="torch.Tensor.unbind"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.unbind()</span></code></a>, <a class="reference internal" href="generated/torch.unbind.html#torch.unbind" title="torch.unbind"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.unbind()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="named_tensor.html#torch.Tensor.unflatten" title="torch.Tensor.unflatten"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.unflatten()</span></code></a></p></td>
<td><p>See documentation</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.uniform_.html#torch.Tensor.uniform_" title="torch.Tensor.uniform_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.uniform_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.var.html#torch.Tensor.var" title="torch.Tensor.var"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.var()</span></code></a>, <a class="reference internal" href="generated/torch.var.html#torch.var" title="torch.var"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.var()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.var_mean.html#torch.var_mean" title="torch.var_mean"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.var_mean()</span></code></a></p></td>
<td><p><a class="reference internal" href="#removes-dimensions-doc"><span class="std std-ref">Removes dimensions</span></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.zero_.html#torch.Tensor.zero_" title="torch.Tensor.zero_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.zero_()</span></code></a></p></td>
<td><p>None</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.zeros.html#torch.zeros" title="torch.zeros"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.zeros()</span></code></a></p></td>
<td><p><a class="reference internal" href="#factory-doc"><span class="std std-ref">Factory functions</span></a></p></td>
</tr>
</tbody>
</table>
<div class="section" id="keeps-input-names">
<span id="keeps-input-names-doc"></span><h2>Keeps input names<a class="headerlink" href="#keeps-input-names" title="Permalink to this headline">¶</a></h2>
<p>All pointwise unary functions follow this rule as well as some other unary functions.</p>
<ul class="simple">
<li><p>Check names: None</p></li>
<li><p>Propagate names: input tensor’s names are propagated to the output.</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">names</span>
<span class="go">(&#39;N&#39;, &#39;C&#39;)</span>
</pre></div>
</div>
</div>
<div class="section" id="removes-dimensions">
<span id="removes-dimensions-doc"></span><h2>Removes dimensions<a class="headerlink" href="#removes-dimensions" title="Permalink to this headline">¶</a></h2>
<p>All reduction ops like <a class="reference internal" href="generated/torch.Tensor.sum.html#torch.Tensor.sum" title="torch.Tensor.sum"><code class="xref py py-meth docutils literal notranslate"><span class="pre">sum()</span></code></a> remove dimensions by reducing
over the desired dimensions. Other operations like <a class="reference internal" href="generated/torch.Tensor.select.html#torch.Tensor.select" title="torch.Tensor.select"><code class="xref py py-meth docutils literal notranslate"><span class="pre">select()</span></code></a> and
<a class="reference internal" href="generated/torch.Tensor.squeeze.html#torch.Tensor.squeeze" title="torch.Tensor.squeeze"><code class="xref py py-meth docutils literal notranslate"><span class="pre">squeeze()</span></code></a> remove dimensions.</p>
<p>Wherever one can pass an integer dimension index to an operator, one can also pass
a dimension name. Functions that take lists of dimension indices can also take in a
list of dimension names.</p>
<ul class="simple">
<li><p>Check names: If <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">dims</span></code> is passed in as a list of names,
check that those names exist in <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>.</p></li>
<li><p>Propagate names: If the dimensions of the input tensor specified by <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>
or <code class="xref py py-attr docutils literal notranslate"><span class="pre">dims</span></code> are not present in the output tensor, then the corresponding names
of those dimensions do not appear in <code class="docutils literal notranslate"><span class="pre">output.names</span></code>.</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;W&#39;</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="go">(&#39;C&#39;, &#39;H&#39;, &#39;W&#39;)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;W&#39;</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">names</span>
<span class="go">(&#39;H&#39;, &#39;W&#39;)</span>

<span class="go"># Reduction ops with keepdim=True don&#39;t actually remove dimensions.</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;W&#39;</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">],</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="go">(&#39;N&#39;, &#39;C&#39;, &#39;H&#39;, &#39;W&#39;)</span>
</pre></div>
</div>
</div>
<div class="section" id="unifies-names-from-inputs">
<span id="unifies-names-from-inputs-doc"></span><h2>Unifies names from inputs<a class="headerlink" href="#unifies-names-from-inputs" title="Permalink to this headline">¶</a></h2>
<p>All binary arithmetic ops follow this rule. Operations that broadcast still
broadcast positionally from the right to preserve compatibility with unnamed
tensors. To perform explicit broadcasting by names, use <a class="reference internal" href="named_tensor.html#torch.Tensor.align_as" title="torch.Tensor.align_as"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.align_as()</span></code></a>.</p>
<ul class="simple">
<li><p>Check names: All names must match positionally from the right. i.e., in
<code class="docutils literal notranslate"><span class="pre">tensor</span> <span class="pre">+</span> <span class="pre">other</span></code>, <code class="docutils literal notranslate"><span class="pre">match(tensor.names[i],</span> <span class="pre">other.names[i])</span></code> must be true for all
<code class="docutils literal notranslate"><span class="pre">i</span></code> in <code class="docutils literal notranslate"><span class="pre">(-min(tensor.dim(),</span> <span class="pre">other.dim())</span> <span class="pre">+</span> <span class="pre">1,</span> <span class="pre">-1]</span></code>.</p></li>
<li><p>Check names: Furthermore, all named dimensions must be aligned from the right.
During matching, if we match a named dimension <code class="docutils literal notranslate"><span class="pre">A</span></code> with an unnamed dimension
<code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">A</span></code> must not appear in the tensor with the unnamed dimension.</p></li>
<li><p>Propagate names: unify pairs of names from the right from both tensors to
produce output names.</p></li>
</ul>
<p>For example,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># tensor: Tensor[   N, None]</span>
<span class="c1"># other:  Tensor[None,    C]</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">other</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">))</span>
<span class="o">&gt;&gt;&gt;</span> <span class="p">(</span><span class="n">tensor</span> <span class="o">+</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Check names:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">match(tensor.names[-1],</span> <span class="pre">other.names[-1])</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">match(tensor.names[-2],</span> <span class="pre">tensor.names[-2])</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
<li><p>Because we matched <code class="docutils literal notranslate"><span class="pre">None</span></code> in <a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code></a> with <code class="docutils literal notranslate"><span class="pre">'C'</span></code>,
check to make sure <code class="docutils literal notranslate"><span class="pre">'C'</span></code> doesn’t exist in <a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code></a> (it does not).</p></li>
<li><p>Check to make sure <code class="docutils literal notranslate"><span class="pre">'N'</span></code> doesn’t exists in <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> (it does not).</p></li>
</ul>
<p>Finally, the output names are computed with
<code class="docutils literal notranslate"><span class="pre">[unify('N',</span> <span class="pre">None),</span> <span class="pre">unify(None,</span> <span class="pre">'C')]</span> <span class="pre">=</span> <span class="pre">['N',</span> <span class="pre">'C']</span></code></p>
<p>More examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Dimensions don&#39;t match from the right:</span>
<span class="c1"># tensor: Tensor[N, C]</span>
<span class="c1"># other:  Tensor[   N]</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">))</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">other</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,))</span>
<span class="o">&gt;&gt;&gt;</span> <span class="p">(</span><span class="n">tensor</span> <span class="o">+</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="ne">RuntimeError</span><span class="p">:</span> <span class="n">Error</span> <span class="n">when</span> <span class="n">attempting</span> <span class="n">to</span> <span class="n">broadcast</span> <span class="n">dims</span> <span class="p">[</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">dims</span>
<span class="p">[</span><span class="s1">&#39;N&#39;</span><span class="p">]:</span> <span class="n">dim</span> <span class="s1">&#39;C&#39;</span> <span class="ow">and</span> <span class="n">dim</span> <span class="s1">&#39;N&#39;</span> <span class="n">are</span> <span class="n">at</span> <span class="n">the</span> <span class="n">same</span> <span class="n">position</span> <span class="kn">from</span> <span class="nn">the</span> <span class="n">right</span> <span class="n">but</span> <span class="n">do</span>
<span class="ow">not</span> <span class="n">match</span><span class="o">.</span>

<span class="c1"># Dimensions aren&#39;t aligned when matching tensor.names[-1] and other.names[-1]:</span>
<span class="c1"># tensor: Tensor[N, None]</span>
<span class="c1"># other:  Tensor[      N]</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">other</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,))</span>
<span class="o">&gt;&gt;&gt;</span> <span class="p">(</span><span class="n">tensor</span> <span class="o">+</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="ne">RuntimeError</span><span class="p">:</span> <span class="n">Misaligned</span> <span class="n">dims</span> <span class="n">when</span> <span class="n">attempting</span> <span class="n">to</span> <span class="n">broadcast</span> <span class="n">dims</span> <span class="p">[</span><span class="s1">&#39;N&#39;</span><span class="p">]</span> <span class="ow">and</span>
<span class="n">dims</span> <span class="p">[</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span> <span class="n">dim</span> <span class="s1">&#39;N&#39;</span> <span class="n">appears</span> <span class="ow">in</span> <span class="n">a</span> <span class="n">different</span> <span class="n">position</span> <span class="kn">from</span> <span class="nn">the</span> <span class="n">right</span>
<span class="n">across</span> <span class="n">both</span> <span class="n">lists</span><span class="o">.</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In both of the last examples, it is possible to align the tensors by names
and then perform the addition. Use <a class="reference internal" href="named_tensor.html#torch.Tensor.align_as" title="torch.Tensor.align_as"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.align_as()</span></code></a> to align
tensors by name or <a class="reference internal" href="named_tensor.html#torch.Tensor.align_to" title="torch.Tensor.align_to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.align_to()</span></code></a> to align tensors to a custom
dimension ordering.</p>
</div>
</div>
<div class="section" id="permutes-dimensions">
<span id="permutes-dimensions-doc"></span><h2>Permutes dimensions<a class="headerlink" href="#permutes-dimensions" title="Permalink to this headline">¶</a></h2>
<p>Some operations, like <a class="reference internal" href="generated/torch.Tensor.t.html#torch.Tensor.t" title="torch.Tensor.t"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.t()</span></code></a>, permute the order of dimensions. Dimension names
are attached to individual dimensions so they get permuted as well.</p>
<p>If the operator takes in positional index <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>, it is also able to take a dimension
name as <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>.</p>
<ul class="simple">
<li><p>Check names: If <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is passed as a name, check that it exists in the tensor.</p></li>
<li><p>Propagate names: Permute dimension names in the same way as the dimensions that are
being permuted.</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="go">(&#39;C&#39;, &#39;N&#39;)</span>
</pre></div>
</div>
</div>
<div class="section" id="contracts-away-dims">
<span id="contracts-away-dims-doc"></span><h2>Contracts away dims<a class="headerlink" href="#contracts-away-dims" title="Permalink to this headline">¶</a></h2>
<p>Matrix multiply functions follow some variant of this. Let’s go through
<a class="reference internal" href="generated/torch.mm.html#torch.mm" title="torch.mm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mm()</span></code></a> first and then generalize the rule for batch matrix multiplication.</p>
<p>For <code class="docutils literal notranslate"><span class="pre">torch.mm(tensor,</span> <span class="pre">other)</span></code>:</p>
<ul class="simple">
<li><p>Check names: None</p></li>
<li><p>Propagate names: result names are <code class="docutils literal notranslate"><span class="pre">(tensor.names[-2],</span> <span class="pre">other.names[-1])</span></code>.</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;in&#39;</span><span class="p">,</span> <span class="s1">&#39;out&#39;</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="go">(&#39;N&#39;, &#39;out&#39;)</span>
</pre></div>
</div>
<p>Inherently, a matrix multiplication performs a dot product over two dimensions,
collapsing them. When two tensors are matrix-multiplied, the contracted dimensions
disappear and do not show up in the output tensor.</p>
<p><a class="reference internal" href="generated/torch.mv.html#torch.mv" title="torch.mv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mv()</span></code></a>, <a class="reference internal" href="generated/torch.dot.html#torch.dot" title="torch.dot"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.dot()</span></code></a> work in a similar way: name inference does not
check input names and removes the dimensions that are involved in the dot product:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;something&#39;</span><span class="p">,))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">mv</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="go">(&#39;N&#39;,)</span>
</pre></div>
</div>
<p>Now, let’s take a look at <code class="docutils literal notranslate"><span class="pre">torch.matmul(tensor,</span> <span class="pre">other)</span></code>. Assume that <code class="docutils literal notranslate"><span class="pre">tensor.dim()</span> <span class="pre">&gt;=</span> <span class="pre">2</span></code>
and <code class="docutils literal notranslate"><span class="pre">other.dim()</span> <span class="pre">&gt;=</span> <span class="pre">2</span></code>.</p>
<ul class="simple">
<li><p>Check names: Check that the batch dimensions of the inputs are aligned and broadcastable.
See <a class="reference internal" href="#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a> for what it means for the inputs to be aligned.</p></li>
<li><p>Propagate names: result names are obtained by unifying the batch dimensions and removing
the contracted dimensions:
<code class="docutils literal notranslate"><span class="pre">unify(tensor.names[:-2],</span> <span class="pre">other.names[:-2])</span> <span class="pre">+</span> <span class="pre">(tensor.names[-2],</span> <span class="pre">other.names[-1])</span></code>.</p></li>
</ul>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Batch matrix multiply of matrices Tensor[&#39;C&#39;, &#39;D&#39;] and Tensor[&#39;E&#39;, &#39;F&#39;].</span>
<span class="c1"># &#39;A&#39;, &#39;B&#39; are batch dimensions.</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">))</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">))</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Finally, there are fused <code class="docutils literal notranslate"><span class="pre">add</span></code> versions of many matmul functions. i.e., <a class="reference internal" href="generated/torch.addmm.html#torch.addmm" title="torch.addmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">addmm()</span></code></a>
and <a class="reference internal" href="generated/torch.addmv.html#torch.addmv" title="torch.addmv"><code class="xref py py-func docutils literal notranslate"><span class="pre">addmv()</span></code></a>. These are treated as composing name inference for i.e. <a class="reference internal" href="generated/torch.mm.html#torch.mm" title="torch.mm"><code class="xref py py-func docutils literal notranslate"><span class="pre">mm()</span></code></a> and
name inference for <a class="reference internal" href="generated/torch.add.html#torch.add" title="torch.add"><code class="xref py py-func docutils literal notranslate"><span class="pre">add()</span></code></a>.</p>
</div>
<div class="section" id="factory-functions">
<span id="factory-doc"></span><h2>Factory functions<a class="headerlink" href="#factory-functions" title="Permalink to this headline">¶</a></h2>
<p>Factory functions now take a new <code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code> argument that associates a name
with each dimension.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">))</span>
<span class="go">tensor([[0., 0., 0.],</span>
<span class="go">        [0., 0., 0.]], names=(&#39;N&#39;, &#39;C&#39;))</span>
</pre></div>
</div>
</div>
<div class="section" id="out-function-and-in-place-variants">
<span id="out-function-semantics-doc"></span><h2>out function and in-place variants<a class="headerlink" href="#out-function-and-in-place-variants" title="Permalink to this headline">¶</a></h2>
<p>A tensor specified as an <code class="docutils literal notranslate"><span class="pre">out=</span></code> tensor has the following behavior:</p>
<ul class="simple">
<li><p>If it has no named dimensions, then the names computed from the operation
get propagated to it.</p></li>
<li><p>If it has any named dimensions, then the names computed from the operation
must be exactly equal to the existing names. Otherwise, the operation errors.</p></li>
</ul>
<p>All in-place methods modify inputs to have names equal to the computed names
from name inference. For example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">names</span>
<span class="go">(None, None)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">+=</span> <span class="n">y</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">names</span>
<span class="go">(&#39;N&#39;, &#39;C&#39;)</span>
</pre></div>
</div>
</div>
</div>


             </article>
             
            </div>
            <footer>
  
    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
      
        <a href="__config__.html" class="btn btn-neutral float-right" title="torch.__config__" accesskey="n" rel="next">Next <img src="_static/images/chevron-right-orange.svg" class="next-page"></a>
      
      
        <a href="named_tensor.html" class="btn btn-neutral" title="Named Tensors" accesskey="p" rel="prev"><img src="_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
      
    </div>
  

    <hr>

  
  <div role="contentinfo">
    <p>
        &copy; Copyright 2019, Torch Contributors.

    </p>
  </div>
    
      <div>
        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
      </div>
     

</footer>

          </div>
        </div>

        <div class="pytorch-content-right" id="pytorch-content-right">
          <div class="pytorch-right-menu" id="pytorch-right-menu">
            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
              <ul>
<li><a class="reference internal" href="#">Named Tensors operator coverage</a><ul>
<li><a class="reference internal" href="#keeps-input-names">Keeps input names</a></li>
<li><a class="reference internal" href="#removes-dimensions">Removes dimensions</a></li>
<li><a class="reference internal" href="#unifies-names-from-inputs">Unifies names from inputs</a></li>
<li><a class="reference internal" href="#permutes-dimensions">Permutes dimensions</a></li>
<li><a class="reference internal" href="#contracts-away-dims">Contracts away dims</a></li>
<li><a class="reference internal" href="#factory-functions">Factory functions</a></li>
<li><a class="reference internal" href="#out-function-and-in-place-variants">out function and in-place variants</a></li>
</ul>
</li>
</ul>

            </div>
          </div>
        </div>
      </section>
    </div>

  
       <script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
         <script src="_static/underscore.js"></script>
         <script src="_static/doctools.js"></script>
         <script src="_static/clipboard.min.js"></script>
         <script src="_static/copybutton.js"></script>
     

  <script type="text/javascript" src="_static/js/vendor/popper.min.js"></script>
  <script type="text/javascript" src="_static/js/vendor/bootstrap.min.js"></script>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/list.js/1.5.0/list.min.js"></script>
  <script type="text/javascript" src="_static/js/theme.js"></script>

  <script type="text/javascript">
      jQuery(function () {
          SphinxRtdTheme.Navigation.enable(true);
      });
  </script>
 
<script script type="text/javascript">
  var collapsedSections = ['Notes', 'Language Bindings', 'Libraries', 'Community'];
</script>

<img height="1" width="1" style="border-style:none;" alt="" src="https://www.googleadservices.com/pagead/conversion/795629140/?label=txkmCPmdtosBENSssfsC&amp;guid=ON&amp;script=0"/>


  <!-- Begin Footer -->

  <div class="container-fluid docs-tutorials-resources" id="docs-tutorials-resources">
    <div class="container">
      <div class="row">
        <div class="col-md-4 text-center">
          <h2>Docs</h2>
          <p>Access comprehensive developer documentation for PyTorch</p>
          <a class="with-right-arrow" href="https://pytorch.org/docs/stable/index.html">View Docs</a>
        </div>

        <div class="col-md-4 text-center">
          <h2>Tutorials</h2>
          <p>Get in-depth tutorials for beginners and advanced developers</p>
          <a class="with-right-arrow" href="https://pytorch.org/tutorials">View Tutorials</a>
        </div>

        <div class="col-md-4 text-center">
          <h2>Resources</h2>
          <p>Find development resources and get your questions answered</p>
          <a class="with-right-arrow" href="https://pytorch.org/resources">View Resources</a>
        </div>
      </div>
    </div>
  </div>

  <footer class="site-footer">
    <div class="container footer-container">
      <div class="footer-logo-wrapper">
        <a href="https://pytorch.org/" class="footer-logo"></a>
      </div>

      <div class="footer-links-wrapper">
        <div class="footer-links-col">
          <ul>
            <li class="list-title"><a href="https://pytorch.org/">PyTorch</a></li>
            <li><a href="https://pytorch.org/get-started">Get Started</a></li>
            <li><a href="https://pytorch.org/features">Features</a></li>
            <li><a href="https://pytorch.org/ecosystem">Ecosystem</a></li>
            <li><a href="https://pytorch.org/blog/">Blog</a></li>
            <li><a href="https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md">Contributing</a></li>
          </ul>
        </div>

        <div class="footer-links-col">
          <ul>
            <li class="list-title"><a href="https://pytorch.org/resources">Resources</a></li>
            <li><a href="https://pytorch.org/tutorials">Tutorials</a></li>
            <li><a href="https://pytorch.org/docs/stable/index.html">Docs</a></li>
            <li><a href="https://discuss.pytorch.org" target="_blank">Discuss</a></li>
            <li><a href="https://github.com/pytorch/pytorch/issues" target="_blank">Github Issues</a></li>
            <li><a href="https://pytorch.org/assets/brand-guidelines/PyTorch-Brand-Guidelines.pdf" target="_blank">Brand Guidelines</a></li>
          </ul>
        </div>

        <div class="footer-links-col follow-us-col">
          <ul>
            <li class="list-title">Stay Connected</li>
            <li>
              <div id="mc_embed_signup">
                <form
                  action="https://twitter.us14.list-manage.com/subscribe/post?u=75419c71fe0a935e53dfa4a3f&id=91d0dccd39"
                  method="post"
                  id="mc-embedded-subscribe-form"
                  name="mc-embedded-subscribe-form"
                  class="email-subscribe-form validate"
                  target="_blank"
                  novalidate>
                  <div id="mc_embed_signup_scroll" class="email-subscribe-form-fields-wrapper">
                    <div class="mc-field-group">
                      <label for="mce-EMAIL" style="display:none;">Email Address</label>
                      <input type="email" value="" name="EMAIL" class="required email" id="mce-EMAIL" placeholder="Email Address">
                    </div>

                    <div id="mce-responses" class="clear">
                      <div class="response" id="mce-error-response" style="display:none"></div>
                      <div class="response" id="mce-success-response" style="display:none"></div>
                    </div>    <!-- real people should not fill this in and expect good things - do not remove this or risk form bot signups-->

                    <div style="position: absolute; left: -5000px;" aria-hidden="true"><input type="text" name="b_75419c71fe0a935e53dfa4a3f_91d0dccd39" tabindex="-1" value=""></div>

                    <div class="clear">
                      <input type="submit" value="" name="subscribe" id="mc-embedded-subscribe" class="button email-subscribe-button">
                    </div>
                  </div>
                </form>
              </div>

            </li>
          </ul>

          <div class="footer-social-icons">
            <a href="https://www.facebook.com/pytorch" target="_blank" class="facebook"></a>
            <a href="https://twitter.com/pytorch" target="_blank" class="twitter"></a>
            <a href="https://www.youtube.com/pytorch" target="_blank" class="youtube"></a>
          </div>
        </div>
      </div>
    </div>
  </footer>

  <div class="cookie-banner-wrapper">
  <div class="container">
    <p class="gdpr-notice">To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: <a href="https://www.facebook.com/policies/cookies/">Cookies Policy</a>.</p>
    <img class="close-button" src="_static/images/pytorch-x.svg">
  </div>
</div>

  <!-- End Footer -->

  <!-- Begin Mobile Menu -->

  <div class="mobile-main-menu">
    <div class="container-fluid">
      <div class="container">
        <div class="mobile-main-menu-header-container">
          <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
          <a class="main-menu-close-button" href="#" data-behavior="close-mobile-menu"></a>
        </div>
      </div>
    </div>

    <div class="mobile-main-menu-links-container">
      <div class="main-menu">
        <ul>
          <li>
            <a href="https://pytorch.org/get-started">Get Started</a>
          </li>

          <li>
            <a href="https://pytorch.org/ecosystem">Ecosystem</a>
          </li>

          <li>
            <a href="https://pytorch.org/mobile">Mobile</a>
          </li>

          <li>
            <a href="https://pytorch.org/hub">PyTorch Hub</a>
          </li>

          <li>
            <a href="https://pytorch.org/blog/">Blog</a>
          </li>

          <li>
            <a href="https://pytorch.org/tutorials">Tutorials</a>
          </li>

          <li class="resources-mobile-menu-title" class="active">
            Docs
          </li>

          <ul class="resources-mobile-menu-items">
            <li>
              <a href="https://pytorch.org/docs/stable/index.html">PyTorch</a>
            </li>

            <li>
              <a href="https://pytorch.org/audio/stable/index.html">torchaudio</a>
            </li>

            <li>
              <a href="https://pytorch.org/text/stable/index.html">torchtext</a>
            </li>

            <li>
              <a href="https://pytorch.org/vision/stable/index.html">torchvision</a>
            </li>

            <li>
              <a href="https://pytorch.org/elastic/">TorchElastic</a>
            </li>

            <li>
              <a href="https://pytorch.org/serve/">TorchServe</a>
            </li>

            <li>
              <a href="https://pytorch.org/xla">PyTorch on XLA Devices</a>
            </li>
          </ul>

          <li class="resources-mobile-menu-title">
            Resources
          </li>

          <ul class="resources-mobile-menu-items">
            <li>
              <a href="https://pytorch.org/resources">Developer Resources</a>
            </li>

            <li>
              <a href="https://pytorch.org/features">About</a>
            </li>

            <li>
              <a href="https://pytorch.org/hub">Models (Beta)</a>
            </li>

            <li>
              <a href="https://pytorch.org/#community-module">Community</a>
            </li>

            <li>
              <a href="https://discuss.pytorch.org/">Forums</a>
            </li>
          </ul>

          <li>
            <a href="https://github.com/pytorch/pytorch">Github</a>
          </li>
        </ul>
      </div>
    </div>
  </div>

  <!-- End Mobile Menu -->

  <script type="text/javascript" src="_static/js/vendor/anchor.min.js"></script>

  <script type="text/javascript">
    $(document).ready(function() {
      mobileMenu.bind();
      mobileTOC.bind();
      pytorchAnchors.bind();
      sideMenus.bind();
      scrollToAnchor.bind();
      highlightNavigation.bind();
      mainMenuDropdown.bind();
      filterTags.bind();

      // Add class to links that have code blocks, since we cannot create links in code blocks
      $("article.pytorch-article a span.pre").each(function(e) {
        $(this).closest("a").addClass("has-code");
      });
    })
  </script>
</body>
</html>