HardSigmoid
===========

HardSigmoid takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise.

**Abstract Signature:**

``HardSigmoid(X: Tensor, alpha: float, beta: float)``

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        <div id="PyTorch_0" class="op-tab-pane active"><h4>PyTorch</h4><div class="op-detail-row"><span class="label">API:</span> <code>torch.nn.modules.activation.Hardsigmoid</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Direct Mapping</span></div><div class="op-detail-row"><a href="https://pytorch.org/docs/stable/generated/torch.nn.modules.activation.Hardsigmoid.html" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="Keras_1" class="op-tab-pane "><h4>Keras</h4><div class="op-detail-row"><span class="label">API:</span> <code>keras.src.ops.nn.HardSigmoid</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Direct Mapping</span></div><div class="op-detail-row"><a href="https://keras.io/search.html?q=keras.src.ops.nn.HardSigmoid" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div> 
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