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   <div class="section" id="module-apache_beam.ml.inference.pytorch_inference">
 <span id="apache-beam-ml-inference-pytorch-inference-module"></span><h1>apache_beam.ml.inference.pytorch_inference module<a class="headerlink" href="#module-apache_beam.ml.inference.pytorch_inference" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.pytorch_inference.</code><code class="descname">PytorchModelHandlerTensor</code><span class="sig-paren">(</span><em>state_dict_path: Optional[str] = None, model_class: Optional[Callable[[...], &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee13652400&gt;]] = None, model_params: Optional[Dict[str, Any]] = None, device: str = 'CPU', *, inference_fn: Callable[[Sequence[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee136d9940&gt;], &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee1578eb50&gt;, &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee135f6e50&gt;, Optional[Dict[str, Any]], Optional[str]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_tensor_inference_fn&gt;, torch_script_model_path: Optional[str] = None, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None, large_model: bool = False, load_model_args: Optional[Dict[str, Any]] = None, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerTensor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler" title="apache_beam.ml.inference.base.ModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.base.ModelHandler</span></code></a></p>
 <p>Implementation of the ModelHandler interface for PyTorch.</p>
 <dl class="docutils">
 <dt>Example Usage for torch model::</dt>
 <dd><dl class="first last docutils">
 <dt>pcoll | RunInference(PytorchModelHandlerTensor(state_dict_path=”my_uri”,</dt>
 <dd>model_class=”my_class”))</dd>
 </dl>
 </dd>
 <dt>Example Usage for torchscript model::</dt>
 <dd><dl class="first last docutils">
 <dt>pcoll | RunInference(PytorchModelHandlerTensor(</dt>
 <dd>torch_script_model_path=”my_uri”))</dd>
 </dl>
 </dd>
 </dl>
 <p>See <a class="reference external" href="https://pytorch.org/tutorials/beginner/saving_loading_models.html">https://pytorch.org/tutorials/beginner/saving_loading_models.html</a>
 for details</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>state_dict_path</strong> – path to the saved dictionary of the model state.</li>
 <li><strong>model_class</strong> – class of the Pytorch model that defines the model
 structure.</li>
 <li><strong>model_params</strong> – A dictionary of arguments required to instantiate the model
 class.</li>
 <li><strong>device</strong> – the device on which you wish to run the model. If
 <code class="docutils literal notranslate"><span class="pre">device</span> <span class="pre">=</span> <span class="pre">GPU</span></code> then a GPU device will be used if it is available.
 Otherwise, it will be CPU.</li>
 <li><strong>inference_fn</strong> – the inference function to use during RunInference.
 default=_default_tensor_inference_fn</li>
 <li><strong>torch_script_model_path</strong> – <dl class="docutils">
 <dt>Path to the torch script model.</dt>
 <dd>the model will be loaded using <cite>torch.jit.load()</cite>.</dd>
 <dt><cite>state_dict_path</cite>, <cite>model_class</cite> and <cite>model_params</cite></dt>
 <dd>arguments will be disregarded.</dd>
 </dl>
 </li>
 <li><strong>min_batch_size</strong> – the minimum batch size to use when batching inputs. This
 batch will be fed into the inference_fn as a Sequence of Tensors.</li>
 <li><strong>max_batch_size</strong> – the maximum batch size to use when batching inputs. This
 batch will be fed into the inference_fn as a Sequence of Tensors.</li>
 <li><strong>large_model</strong> – set to true if your model is large enough to run into
 memory pressure if you load multiple copies. Given a model that
 consumes N memory and a machine with W cores and M memory, you should
 set this to True if N*W &gt; M.</li>
 <li><strong>load_model_args</strong> – a dictionary of parameters passed to the torch.load
 function to specify custom config for loading models.</li>
 <li><strong>kwargs</strong> – ‘env_vars’ can be used to set environment variables
 before loading the model.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <p><strong>Supported Versions:</strong> RunInference APIs in Apache Beam have been tested
 with PyTorch 1.9 and 1.10.</p>
 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.load_model">
 <code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee14ecba90&gt;<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerTensor.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.load_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads and initializes a Pytorch model for processing.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.update_model_path">
 <code class="descname">update_model_path</code><span class="sig-paren">(</span><em>model_path: Optional[str] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerTensor.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.update_model_path" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee14e21f70&gt;], model: &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee132f12b0&gt;, inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerTensor.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs inferences on a batch of Tensors and returns an Iterable of
 Tensor Predictions.</p>
 <p>This method stacks the list of Tensors in a vectorized format to optimize
 the inference call.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
 <li><strong>batch</strong> – A sequence of Tensors. These Tensors should be batchable, as this
 method will call <cite>torch.stack()</cite> and pass in batched Tensors with
 dimensions (batch_size, n_features, etc.) into the model’s forward()
 function.</li>
 <li><strong>model</strong> – A PyTorch model.</li>
 <li><strong>inference_args</strong> – Non-batchable arguments required as inputs to the model’s
 forward() function. Unlike Tensors in <cite>batch</cite>, these parameters will
 not be dynamically batched</li>
 </ul>
 </td>
 </tr>
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">An Iterable of type PredictionResult.</p>
 </td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.get_num_bytes">
 <code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee132f11c0&gt;]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerTensor.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.get_num_bytes" title="Permalink to this definition">¶</a></dt>
 <dd><table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The number of bytes of data for a batch of Tensors.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.get_metrics_namespace">
 <code class="descname">get_metrics_namespace</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; str<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerTensor.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.get_metrics_namespace" title="Permalink to this definition">¶</a></dt>
 <dd><table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A namespace for metrics collected by the RunInference transform.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.validate_inference_args">
 <code class="descname">validate_inference_args</code><span class="sig-paren">(</span><em>inference_args: Optional[Dict[str, Any]]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerTensor.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.validate_inference_args" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.batch_elements_kwargs">
 <code class="descname">batch_elements_kwargs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerTensor.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.batch_elements_kwargs" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.share_model_across_processes">
 <code class="descname">share_model_across_processes</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; bool<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerTensor.share_model_across_processes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerTensor.share_model_across_processes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.pytorch_inference.</code><code class="descname">PytorchModelHandlerKeyedTensor</code><span class="sig-paren">(</span><em>state_dict_path: Optional[str] = None, model_class: Optional[Callable[[...], &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee135f52b0&gt;]] = None, model_params: Optional[Dict[str, Any]] = None, device: str = 'CPU', *, inference_fn: Callable[[Sequence[Dict[str, &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee135f6220&gt;]], &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee136a5820&gt;, &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee13bfe2b0&gt;, Optional[Dict[str, Any]], Optional[str]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_keyed_tensor_inference_fn&gt;, torch_script_model_path: Optional[str] = None, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None, large_model: bool = False, load_model_args: Optional[Dict[str, Any]] = None, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerKeyedTensor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler" title="apache_beam.ml.inference.base.ModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.base.ModelHandler</span></code></a></p>
 <p>Implementation of the ModelHandler interface for PyTorch.</p>
 <blockquote>
 <div><dl class="docutils">
 <dt>Example Usage for torch model::</dt>
 <dd><dl class="first last docutils">
 <dt>pcoll | RunInference(PytorchModelHandlerKeyedTensor(</dt>
 <dd>state_dict_path=”my_uri”,
 model_class=”my_class”))</dd>
 </dl>
 </dd>
 </dl>
 </div></blockquote>
 <dl class="docutils">
 <dt>Example Usage for torchscript model::</dt>
 <dd><dl class="first last docutils">
 <dt>pcoll | RunInference(PytorchModelHandlerKeyedTensor(</dt>
 <dd>torch_script_model_path=”my_uri”))</dd>
 </dl>
 </dd>
 </dl>
 <p><strong>NOTE:</strong> This API and its implementation are under development and
 do not provide backward compatibility guarantees.</p>
 <p>See <a class="reference external" href="https://pytorch.org/tutorials/beginner/saving_loading_models.html">https://pytorch.org/tutorials/beginner/saving_loading_models.html</a>
 for details</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>state_dict_path</strong> – path to the saved dictionary of the model state.</li>
 <li><strong>model_class</strong> – class of the Pytorch model that defines the model
 structure.</li>
 <li><strong>model_params</strong> – A dictionary of arguments required to instantiate the model
 class.</li>
 <li><strong>device</strong> – the device on which you wish to run the model. If
 <code class="docutils literal notranslate"><span class="pre">device</span> <span class="pre">=</span> <span class="pre">GPU</span></code> then a GPU device will be used if it is available.
 Otherwise, it will be CPU.</li>
 <li><strong>inference_fn</strong> – the function to invoke on run_inference.
 default = default_keyed_tensor_inference_fn</li>
 <li><strong>torch_script_model_path</strong> – <dl class="docutils">
 <dt>Path to the torch script model.</dt>
 <dd>the model will be loaded using <cite>torch.jit.load()</cite>.</dd>
 <dt><cite>state_dict_path</cite>, <cite>model_class</cite> and <cite>model_params</cite></dt>
 <dd>arguments will be disregarded.</dd>
 </dl>
 </li>
 <li><strong>min_batch_size</strong> – the minimum batch size to use when batching inputs. This
 batch will be fed into the inference_fn as a Sequence of Keyed Tensors.</li>
 <li><strong>max_batch_size</strong> – the maximum batch size to use when batching inputs. This
 batch will be fed into the inference_fn as a Sequence of Keyed Tensors.</li>
 <li><strong>large_model</strong> – set to true if your model is large enough to run into
 memory pressure if you load multiple copies. Given a model that
 consumes N memory and a machine with W cores and M memory, you should
 set this to True if N*W &gt; M.</li>
 <li><strong>load_model_args</strong> – a dictionary of parameters passed to the torch.load
 function to specify custom config for loading models.</li>
 <li><strong>kwargs</strong> – ‘env_vars’ can be used to set environment variables
 before loading the model.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <p><strong>Supported Versions:</strong> RunInference APIs in Apache Beam have been tested
 on torch&gt;=1.9.0,&lt;1.14.0.</p>
 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.load_model">
 <code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee14ecb0a0&gt;<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerKeyedTensor.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.load_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads and initializes a Pytorch model for processing.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.update_model_path">
 <code class="descname">update_model_path</code><span class="sig-paren">(</span><em>model_path: Optional[str] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerKeyedTensor.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.update_model_path" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[Dict[str, &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee135f5310&gt;]], model: &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee135f5b50&gt;, inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerKeyedTensor.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs inferences on a batch of Keyed Tensors and returns an Iterable of
 Tensor Predictions.</p>
 <p>For the same key across all examples, this will stack all Tensors values
 in a vectorized format to optimize the inference call.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
 <li><strong>batch</strong> – A sequence of keyed Tensors. These Tensors should be batchable,
 as this method will call <cite>torch.stack()</cite> and pass in batched Tensors
 with dimensions (batch_size, n_features, etc.) into the model’s
 forward() function.</li>
 <li><strong>model</strong> – A PyTorch model.</li>
 <li><strong>inference_args</strong> – Non-batchable arguments required as inputs to the model’s
 forward() function. Unlike Tensors in <cite>batch</cite>, these parameters will
 not be dynamically batched</li>
 </ul>
 </td>
 </tr>
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">An Iterable of type PredictionResult.</p>
 </td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.get_num_bytes">
 <code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7fee14dbf460&gt;]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerKeyedTensor.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.get_num_bytes" title="Permalink to this definition">¶</a></dt>
 <dd><table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The number of bytes of data for a batch of Dict of Tensors.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.get_metrics_namespace">
 <code class="descname">get_metrics_namespace</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; str<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerKeyedTensor.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.get_metrics_namespace" title="Permalink to this definition">¶</a></dt>
 <dd><table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A namespace for metrics collected by the RunInference transform.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.validate_inference_args">
 <code class="descname">validate_inference_args</code><span class="sig-paren">(</span><em>inference_args: Optional[Dict[str, Any]]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerKeyedTensor.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.validate_inference_args" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.batch_elements_kwargs">
 <code class="descname">batch_elements_kwargs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerKeyedTensor.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.batch_elements_kwargs" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.share_model_across_processes">
 <code class="descname">share_model_across_processes</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; bool<a class="reference internal" href="_modules/apache_beam/ml/inference/pytorch_inference.html#PytorchModelHandlerKeyedTensor.share_model_across_processes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.pytorch_inference.PytorchModelHandlerKeyedTensor.share_model_across_processes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 </dd></dl>

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