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   <div class="section" id="module-apache_beam.ml.inference.tensorflow_inference">
 <span id="apache-beam-ml-inference-tensorflow-inference-module"></span><h1>apache_beam.ml.inference.tensorflow_inference module<a class="headerlink" href="#module-apache_beam.ml.inference.tensorflow_inference" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.tensorflow_inference.</code><code class="descname">TFModelHandlerNumpy</code><span class="sig-paren">(</span><em>model_uri: str, model_type: apache_beam.ml.inference.tensorflow_inference.ModelType = &lt;ModelType.SAVED_MODEL: 1&gt;, create_model_fn: Optional[Callable] = None, *, load_model_args: Optional[Dict[str, Any]] = None, custom_weights: str = '', inference_fn: Callable[[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f7380a26400&gt;, Sequence[Union[numpy.ndarray, &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f7380a26940&gt;]], Dict[str, Any], Optional[str]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_numpy_inference_fn&gt;, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None, max_batch_duration_secs: Optional[int] = None, large_model: bool = False, model_copies: Optional[int] = None, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerNumpy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy" 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 Tensorflow.</p>
 <p>Example Usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span><span class="n">TFModelHandlerNumpy</span><span class="p">(</span><span class="n">model_uri</span><span class="o">=</span><span class="s2">&quot;my_uri&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
 <p>See <a class="reference external" href="https://www.tensorflow.org/tutorials/keras/save_and_load">https://www.tensorflow.org/tutorials/keras/save_and_load</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>model_uri</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – path to the trained model.</li>
 <li><strong>model_type</strong> – type of model to be loaded. Defaults to SAVED_MODEL.</li>
 <li><strong>create_model_fn</strong> – a function that creates and returns a new
 tensorflow model to load the saved weights.
 It should be used with ModelType.SAVED_WEIGHTS.</li>
 <li><strong>load_model_args</strong> – a dictionary of parameters to pass to the load_model
 function of TensorFlow to specify custom config.</li>
 <li><strong>custom_weights</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – path to the custom weights to be applied
 once the model is loaded.</li>
 <li><strong>inference_fn</strong> – inference function to use during RunInference.
 Defaults to default_numpy_inference_fn.</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>model_copies</strong> – The exact number of models that you would like loaded
 onto your machine. This can be useful if you exactly know your CPU or
 GPU capacity and want to maximize resource utilization.</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 Tensorflow 2.9, 2.10, 2.11.</p>
 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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 0x7f7380a32ca0&gt;<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerNumpy.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.load_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads and initializes a Tensorflow model for processing.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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/tensorflow_inference.html#TFModelHandlerNumpy.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.update_model_path" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray], model: &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f7380a32b80&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/tensorflow_inference.html#TFModelHandlerNumpy.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs inferences on a batch of numpy array and returns an Iterable of
 numpy array Predictions.</p>
 <p>This method stacks the n-dimensional numpy array 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 numpy nd-array. These should be batchable, as this
 method will call <cite>numpy.stack()</cite> and pass in batched numpy nd-array
 with dimensions (batch_size, n_features, etc.) into the model’s
 predict() function.</li>
 <li><strong>model</strong> – A Tensorflow model.</li>
 <li><strong>inference_args</strong> – any additional arguments for an inference.</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.tensorflow_inference.TFModelHandlerNumpy.get_num_bytes">
 <code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerNumpy.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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 numpy arrays.</td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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/tensorflow_inference.html#TFModelHandlerNumpy.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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.tensorflow_inference.TFModelHandlerNumpy.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/tensorflow_inference.html#TFModelHandlerNumpy.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.validate_inference_args" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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/tensorflow_inference.html#TFModelHandlerNumpy.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.batch_elements_kwargs" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.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/tensorflow_inference.html#TFModelHandlerNumpy.share_model_across_processes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerNumpy.share_model_across_processes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

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

 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor">
 <em class="property">class </em><code class="descclassname">apache_beam.ml.inference.tensorflow_inference.</code><code class="descname">TFModelHandlerTensor</code><span class="sig-paren">(</span><em>model_uri: str, model_type: apache_beam.ml.inference.tensorflow_inference.ModelType = &lt;ModelType.SAVED_MODEL: 1&gt;, create_model_fn: Optional[Callable] = None, *, load_model_args: Optional[Dict[str, Any]] = None, custom_weights: str = '', inference_fn: Callable[[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f7380a26400&gt;, Sequence[Union[numpy.ndarray, &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f7380a26940&gt;]], Dict[str, Any], Optional[str]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = &lt;function default_tensor_inference_fn&gt;, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None, max_batch_duration_secs: Optional[int] = None, large_model: bool = False, model_copies: Optional[int] = None, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerTensor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor" 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 Tensorflow.</p>
 <p>Example Usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span><span class="n">TFModelHandlerTensor</span><span class="p">(</span><span class="n">model_uri</span><span class="o">=</span><span class="s2">&quot;my_uri&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
 <p>See <a class="reference external" href="https://www.tensorflow.org/tutorials/keras/save_and_load">https://www.tensorflow.org/tutorials/keras/save_and_load</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>model_uri</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – path to the trained model.</li>
 <li><strong>model_type</strong> – type of model to be loaded.
 Defaults to SAVED_MODEL.</li>
 <li><strong>create_model_fn</strong> – a function that creates and returns a new
 tensorflow model to load the saved weights.
 It should be used with ModelType.SAVED_WEIGHTS.</li>
 <li><strong>load_model_args</strong> – a dictionary of parameters to pass to the load_model
 function of TensorFlow to specify custom config.</li>
 <li><strong>custom_weights</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – path to the custom weights to be applied
 once the model is loaded.</li>
 <li><strong>inference_fn</strong> – inference function to use during RunInference.
 Defaults to default_numpy_inference_fn.</li>
 <li><strong>min_batch_size</strong> – the minimum batch size to use when batching inputs.</li>
 <li><strong>max_batch_size</strong> – the maximum batch size to use when batching inputs.</li>
 <li><strong>max_batch_duration_secs</strong> – the maximum amount of time to buffer a batch
 before emitting; used in streaming contexts.</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>model_copies</strong> – The exact number of models that you would like loaded
 onto your machine. This can be useful if you exactly know your CPU or
 GPU capacity and want to maximize resource utilization.</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 Tensorflow 2.11.</p>
 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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 0x7f738084eaf0&gt;<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerTensor.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.load_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads and initializes a tensorflow model for processing.</p>
 </dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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/tensorflow_inference.html#TFModelHandlerTensor.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.update_model_path" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.run_inference">
 <code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[&lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f738084eb50&gt;], model: &lt;sphinx.ext.autodoc.importer._MockObject object at 0x7f738097ba60&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/tensorflow_inference.html#TFModelHandlerTensor.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.run_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs inferences on a batch of tf.Tensor 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>tf.stack()</cite> and pass in batched Tensors with
 dimensions (batch_size, n_features, etc.) into the model’s predict()
 function.</li>
 <li><strong>model</strong> – A Tensorflow 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.tensorflow_inference.TFModelHandlerTensor.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 0x7f738097b280&gt;]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/tensorflow_inference.html#TFModelHandlerTensor.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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.tensorflow_inference.TFModelHandlerTensor.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/tensorflow_inference.html#TFModelHandlerTensor.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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.tensorflow_inference.TFModelHandlerTensor.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/tensorflow_inference.html#TFModelHandlerTensor.validate_inference_args"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.validate_inference_args" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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/tensorflow_inference.html#TFModelHandlerTensor.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.batch_elements_kwargs" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

 <dl class="method">
 <dt id="apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.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/tensorflow_inference.html#TFModelHandlerTensor.share_model_across_processes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.tensorflow_inference.TFModelHandlerTensor.share_model_across_processes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>

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

 </dd></dl>

 </div>


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