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<h1>Source code for torch</h1><div class="highlight"><pre>
<span></span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd">The torch package contains data structures for multi-dimensional</span>
<span class="sd">tensors and mathematical operations over these are defined.</span>
<span class="sd">Additionally, it provides many utilities for efficient serializing of</span>
<span class="sd">Tensors and arbitrary types, and other useful utilities.</span>
<span class="sd">It has a CUDA counterpart, that enables you to run your tensor computations</span>
<span class="sd">on an NVIDIA GPU with compute capability >= 3.0.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">platform</span>
<span class="kn">from</span> <span class="nn">._utils</span> <span class="k">import</span> <span class="n">_import_dotted_name</span>
<span class="kn">from</span> <span class="nn">._utils_internal</span> <span class="k">import</span> <span class="n">get_file_path</span><span class="p">,</span> <span class="n">prepare_multiprocessing_environment</span>
<span class="kn">from</span> <span class="nn">.version</span> <span class="k">import</span> <span class="n">__version__</span>
<span class="kn">from</span> <span class="nn">._six</span> <span class="k">import</span> <span class="n">string_classes</span> <span class="k">as</span> <span class="n">_string_classes</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s1">'typename'</span><span class="p">,</span> <span class="s1">'is_tensor'</span><span class="p">,</span> <span class="s1">'is_storage'</span><span class="p">,</span> <span class="s1">'set_default_tensor_type'</span><span class="p">,</span>
<span class="s1">'set_rng_state'</span><span class="p">,</span> <span class="s1">'get_rng_state'</span><span class="p">,</span> <span class="s1">'manual_seed'</span><span class="p">,</span> <span class="s1">'initial_seed'</span><span class="p">,</span>
<span class="s1">'save'</span><span class="p">,</span> <span class="s1">'load'</span><span class="p">,</span> <span class="s1">'set_printoptions'</span><span class="p">,</span> <span class="s1">'chunk'</span><span class="p">,</span> <span class="s1">'split'</span><span class="p">,</span> <span class="s1">'stack'</span><span class="p">,</span> <span class="s1">'matmul'</span><span class="p">,</span>
<span class="s1">'no_grad'</span><span class="p">,</span> <span class="s1">'enable_grad'</span><span class="p">,</span> <span class="s1">'rand'</span><span class="p">,</span> <span class="s1">'randn'</span><span class="p">,</span>
<span class="s1">'DoubleStorage'</span><span class="p">,</span> <span class="s1">'FloatStorage'</span><span class="p">,</span> <span class="s1">'LongStorage'</span><span class="p">,</span> <span class="s1">'IntStorage'</span><span class="p">,</span>
<span class="s1">'ShortStorage'</span><span class="p">,</span> <span class="s1">'CharStorage'</span><span class="p">,</span> <span class="s1">'ByteStorage'</span><span class="p">,</span>
<span class="s1">'DoubleTensor'</span><span class="p">,</span> <span class="s1">'FloatTensor'</span><span class="p">,</span> <span class="s1">'LongTensor'</span><span class="p">,</span> <span class="s1">'IntTensor'</span><span class="p">,</span>
<span class="s1">'ShortTensor'</span><span class="p">,</span> <span class="s1">'CharTensor'</span><span class="p">,</span> <span class="s1">'ByteTensor'</span><span class="p">,</span> <span class="s1">'Tensor'</span><span class="p">,</span>
<span class="p">]</span>
<span class="c1">################################################################################</span>
<span class="c1"># Load the extension module</span>
<span class="c1">################################################################################</span>
<span class="c1"># Loading the extension with RTLD_GLOBAL option allows to not link extension</span>
<span class="c1"># modules against the _C shared object. Their missing THP symbols will be</span>
<span class="c1"># automatically filled by the dynamic loader.</span>
<span class="kn">import</span> <span class="nn">os</span> <span class="k">as</span> <span class="nn">_dl_flags</span>
<span class="c1"># if we have numpy, it *must* be imported before the call to setdlopenflags()</span>
<span class="c1"># or there is risk that later c modules will segfault when importing numpy</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">_np</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="k">pass</span>
<span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">'Windows'</span><span class="p">:</span>
<span class="c1"># first get nvToolsExt PATH</span>
<span class="k">def</span> <span class="nf">get_nvToolsExt_path</span><span class="p">():</span>
<span class="n">NVTOOLEXT_HOME</span> <span class="o">=</span> <span class="n">_dl_flags</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'NVTOOLSEXT_PATH'</span><span class="p">,</span> <span class="s1">'C:</span><span class="se">\\</span><span class="s1">Program Files</span><span class="se">\\</span><span class="s1">NVIDIA Corporation</span><span class="se">\\</span><span class="s1">NvToolsExt'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">_dl_flags</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">NVTOOLEXT_HOME</span><span class="p">):</span>
<span class="k">return</span> <span class="n">NVTOOLEXT_HOME</span> <span class="o">+</span> <span class="s1">'</span><span class="se">\\</span><span class="s1">bin</span><span class="se">\\</span><span class="s1">x64</span><span class="se">\\</span><span class="s1">'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">''</span>
<span class="n">py_dll_path</span> <span class="o">=</span> <span class="n">_dl_flags</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">_dl_flags</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">executable</span><span class="p">),</span> <span class="s1">'Library</span><span class="se">\\</span><span class="s1">bin'</span><span class="p">)</span>
<span class="n">th_dll_path</span> <span class="o">=</span> <span class="n">_dl_flags</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="vm">__file__</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'</span><span class="se">\\</span><span class="s1">lib</span><span class="se">\\</span><span class="s1">'</span>
<span class="n">dll_paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="n">py_dll_path</span><span class="p">,</span> <span class="n">get_nvToolsExt_path</span><span class="p">(),</span> <span class="n">_dl_flags</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PATH'</span><span class="p">]]</span>
<span class="c1"># then add the path to env</span>
<span class="n">_dl_flags</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PATH'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">';'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dll_paths</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># first check if the os package has the required flags</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_dl_flags</span><span class="p">,</span> <span class="s1">'RTLD_GLOBAL'</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_dl_flags</span><span class="p">,</span> <span class="s1">'RTLD_LAZY'</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="c1"># next try if DLFCN exists</span>
<span class="kn">import</span> <span class="nn">DLFCN</span> <span class="k">as</span> <span class="nn">_dl_flags</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="c1"># as a last attempt, use compile-time constants</span>
<span class="kn">import</span> <span class="nn">torch._dl</span> <span class="k">as</span> <span class="nn">_dl_flags</span>
<span class="n">old_flags</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">getdlopenflags</span><span class="p">()</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setdlopenflags</span><span class="p">(</span><span class="n">_dl_flags</span><span class="o">.</span><span class="n">RTLD_GLOBAL</span> <span class="o">|</span> <span class="n">_dl_flags</span><span class="o">.</span><span class="n">RTLD_LAZY</span><span class="p">)</span>
<span class="k">del</span> <span class="n">_dl_flags</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">torch._nvrtc</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="k">pass</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="k">import</span> <span class="o">*</span>
<span class="n">__all__</span> <span class="o">+=</span> <span class="p">[</span><span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">_C</span><span class="p">)</span>
<span class="k">if</span> <span class="n">name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">'_'</span> <span class="ow">and</span>
<span class="ow">not</span> <span class="n">name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'Base'</span><span class="p">)]</span>
<span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">!=</span> <span class="s1">'Windows'</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setdlopenflags</span><span class="p">(</span><span class="n">old_flags</span><span class="p">)</span>
<span class="k">del</span> <span class="n">old_flags</span>
<span class="c1">################################################################################</span>
<span class="c1"># Define basic utilities</span>
<span class="c1">################################################################################</span>
<span class="k">def</span> <span class="nf">typename</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">type</span><span class="p">()</span>
<span class="n">module</span> <span class="o">=</span> <span class="s1">''</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="s1">''</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">'__module__'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">!=</span> <span class="s1">'builtins'</span> \
<span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">!=</span> <span class="s1">'__builtin__'</span> <span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">+</span> <span class="s1">'.'</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">'__qualname__'</span><span class="p">):</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__qualname__</span>
<span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">'__name__'</span><span class="p">):</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__name__</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
<span class="k">return</span> <span class="n">module</span> <span class="o">+</span> <span class="n">class_name</span>
<div class="viewcode-block" id="is_tensor"><a class="viewcode-back" href="../torch.html#torch.is_tensor">[docs]</a><span class="k">def</span> <span class="nf">is_tensor</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Returns True if `obj` is a PyTorch tensor.</span>
<span class="sd"> Args:</span>
<span class="sd"> obj (Object): Object to test</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span></div>
<div class="viewcode-block" id="is_storage"><a class="viewcode-back" href="../torch.html#torch.is_storage">[docs]</a><span class="k">def</span> <span class="nf">is_storage</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Returns True if `obj` is a PyTorch storage object.</span>
<span class="sd"> Args:</span>
<span class="sd"> obj (Object): Object to test</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span> <span class="ow">in</span> <span class="n">_storage_classes</span></div>
<div class="viewcode-block" id="set_default_tensor_type"><a class="viewcode-back" href="../torch.html#torch.set_default_tensor_type">[docs]</a><span class="k">def</span> <span class="nf">set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Sets the default ``torch.Tensor`` type to floating point tensor type</span>
<span class="sd"> :attr:`t`. This type will also be used as default floating point type for</span>
<span class="sd"> type inference in :func:`torch.tensor`.</span>
<span class="sd"> The default floating point tensor type is initially ``torch.FloatTensor``.</span>
<span class="sd"> Args:</span>
<span class="sd"> t (type or string): the floating point tensor type or its name</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # initial default for floating point is torch.float32</span>
<span class="sd"> torch.float32</span>
<span class="sd"> >>> torch.set_default_tensor_type(torch.DoubleTensor)</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor</span>
<span class="sd"> torch.float64</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">_string_classes</span><span class="p">):</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">_import_dotted_name</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span></div>
<div class="viewcode-block" id="set_default_dtype"><a class="viewcode-back" href="../torch.html#torch.set_default_dtype">[docs]</a><span class="k">def</span> <span class="nf">set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Sets the default floating point dtype to :attr:`d`. This type will be</span>
<span class="sd"> used as default floating point type for type inference in</span>
<span class="sd"> :func:`torch.tensor`.</span>
<span class="sd"> The default floating point dtype is initially ``torch.float32``.</span>
<span class="sd"> Args:</span>
<span class="sd"> d (:class:`torch.dtype`): the floating point dtype to make the default</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # initial default for floating point is torch.float32</span>
<span class="sd"> torch.float32</span>
<span class="sd"> >>> torch.set_default_dtype(torch.float64)</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor</span>
<span class="sd"> torch.float64</span>
<span class="sd"> """</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">)</span></div>
<span class="kn">from</span> <span class="nn">.random</span> <span class="k">import</span> <span class="n">set_rng_state</span><span class="p">,</span> <span class="n">get_rng_state</span><span class="p">,</span> <span class="n">manual_seed</span><span class="p">,</span> <span class="n">initial_seed</span>
<span class="kn">from</span> <span class="nn">.serialization</span> <span class="k">import</span> <span class="n">save</span><span class="p">,</span> <span class="n">load</span>
<span class="kn">from</span> <span class="nn">._tensor_str</span> <span class="k">import</span> <span class="n">set_printoptions</span>
<span class="c1">################################################################################</span>
<span class="c1"># Define Storage and Tensor classes</span>
<span class="c1">################################################################################</span>
<span class="kn">from</span> <span class="nn">.tensor</span> <span class="k">import</span> <span class="n">Tensor</span>
<span class="kn">from</span> <span class="nn">.storage</span> <span class="k">import</span> <span class="n">_StorageBase</span>
<span class="k">class</span> <span class="nc">DoubleStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">DoubleStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<div class="viewcode-block" id="FloatStorage"><a class="viewcode-back" href="../storage.html#torch.FloatStorage">[docs]</a><span class="k">class</span> <span class="nc">FloatStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">FloatStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span></div>
<span class="k">class</span> <span class="nc">HalfStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">HalfStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">LongStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">LongStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">IntStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IntStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">ShortStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">ShortStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">CharStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">CharStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">ByteStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">ByteStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="n">_storage_classes</span> <span class="o">=</span> <span class="p">{</span>
<span class="n">DoubleStorage</span><span class="p">,</span> <span class="n">FloatStorage</span><span class="p">,</span> <span class="n">LongStorage</span><span class="p">,</span> <span class="n">IntStorage</span><span class="p">,</span> <span class="n">ShortStorage</span><span class="p">,</span>
<span class="n">CharStorage</span><span class="p">,</span> <span class="n">ByteStorage</span><span class="p">,</span> <span class="n">HalfStorage</span>
<span class="p">}</span>
<span class="c1"># The _tensor_classes set is initialized by the call to _C._initialize_tensor_type_bindings()</span>
<span class="n">_tensor_classes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="c1">################################################################################</span>
<span class="c1"># Initialize extension</span>
<span class="c1">################################################################################</span>
<span class="k">def</span> <span class="nf">manager_path</span><span class="p">():</span>
<span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">'Windows'</span><span class="p">:</span>
<span class="k">return</span> <span class="sa">b</span><span class="s2">""</span>
<span class="n">path</span> <span class="o">=</span> <span class="n">get_file_path</span><span class="p">(</span><span class="s1">'torch'</span><span class="p">,</span> <span class="s1">'lib'</span><span class="p">,</span> <span class="s1">'torch_shm_manager'</span><span class="p">)</span>
<span class="n">prepare_multiprocessing_environment</span><span class="p">(</span><span class="n">get_file_path</span><span class="p">(</span><span class="s1">'torch'</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Unable to find torch_shm_manager at "</span> <span class="o">+</span> <span class="n">path</span><span class="p">)</span>
<span class="k">return</span> <span class="n">path</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">)</span>
<span class="c1"># Shared memory manager needs to know the exact location of manager executable</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_initExtension</span><span class="p">(</span><span class="n">manager_path</span><span class="p">())</span>
<span class="k">del</span> <span class="n">manager_path</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">_VariableFunctions</span><span class="p">):</span>
<span class="k">if</span> <span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'__'</span><span class="p">):</span>
<span class="k">continue</span>
<span class="nb">globals</span><span class="p">()[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">_VariableFunctions</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
<span class="c1">################################################################################</span>
<span class="c1"># Import interface functions defined in Python</span>
<span class="c1">################################################################################</span>
<span class="c1"># needs to be after the above ATen bindings so we can overwrite from Python side</span>
<span class="kn">from</span> <span class="nn">.functional</span> <span class="k">import</span> <span class="o">*</span>
<span class="c1">################################################################################</span>
<span class="c1"># Remove unnecessary members</span>
<span class="c1">################################################################################</span>
<span class="k">del</span> <span class="n">DoubleStorageBase</span>
<span class="k">del</span> <span class="n">FloatStorageBase</span>
<span class="k">del</span> <span class="n">LongStorageBase</span>
<span class="k">del</span> <span class="n">IntStorageBase</span>
<span class="k">del</span> <span class="n">ShortStorageBase</span>
<span class="k">del</span> <span class="n">CharStorageBase</span>
<span class="k">del</span> <span class="n">ByteStorageBase</span>
<span class="c1">################################################################################</span>
<span class="c1"># Import most common subpackages</span>
<span class="c1">################################################################################</span>
<span class="kn">import</span> <span class="nn">torch.cuda</span>
<span class="kn">import</span> <span class="nn">torch.autograd</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="k">import</span> <span class="n">no_grad</span><span class="p">,</span> <span class="n">enable_grad</span><span class="p">,</span> <span class="n">set_grad_enabled</span>
<span class="kn">import</span> <span class="nn">torch.nn</span>
<span class="kn">import</span> <span class="nn">torch.optim</span>
<span class="kn">import</span> <span class="nn">torch.multiprocessing</span>
<span class="kn">import</span> <span class="nn">torch.sparse</span>
<span class="kn">import</span> <span class="nn">torch.utils.backcompat</span>
<span class="kn">import</span> <span class="nn">torch.onnx</span>
<span class="kn">import</span> <span class="nn">torch.jit</span>
<span class="kn">import</span> <span class="nn">torch.random</span>
<span class="kn">import</span> <span class="nn">torch.distributions</span>
<span class="kn">import</span> <span class="nn">torch.testing</span>
<span class="kn">import</span> <span class="nn">torch.backends.cuda</span>
<span class="kn">import</span> <span class="nn">torch.backends.mkl</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_init_names</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="p">))</span>
<span class="c1"># attach docstrings to torch and tensor functions</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="k">import</span> <span class="n">_torch_docs</span><span class="p">,</span> <span class="n">_tensor_docs</span><span class="p">,</span> <span class="n">_storage_docs</span>
<span class="k">del</span> <span class="n">_torch_docs</span><span class="p">,</span> <span class="n">_tensor_docs</span><span class="p">,</span> <span class="n">_storage_docs</span>
<div class="viewcode-block" id="compiled_with_cxx11_abi"><a class="viewcode-back" href="../torch.html#torch.compiled_with_cxx11_abi">[docs]</a><span class="k">def</span> <span class="nf">compiled_with_cxx11_abi</span><span class="p">():</span>
<span class="sa">r</span><span class="sd">"""Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1"""</span>
<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_GLIBCXX_USE_CXX11_ABI</span></div>
<span class="c1"># Import the ops "namespace"</span>
<span class="kn">from</span> <span class="nn">torch._ops</span> <span class="k">import</span> <span class="n">ops</span>
</pre></div>
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