Baddbmm
=======

Performs a batch matrix-matrix product of matrices in batch1 and batch2. input is added to final result.

**Abstract Signature:**

``Baddbmm(input: Tensor, batch1: Tensor, batch2: Tensor, beta: float = 1.0, alpha: float = 1.0)``

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        <button class="op-tab-btn active" onclick="openOpTab(event, 'PyTorch_0')">PyTorch</button><button class="op-tab-btn " onclick="openOpTab(event, 'JAX (Core)_1')">JAX (Core)</button><button class="op-tab-btn " onclick="openOpTab(event, 'NumPy_2')">NumPy</button><button class="op-tab-btn " onclick="openOpTab(event, 'Keras_3')">Keras</button><button class="op-tab-btn " onclick="openOpTab(event, 'Apple MLX_4')">Apple MLX</button><button class="op-tab-btn " onclick="openOpTab(event, 'Flax NNX_5')">Flax NNX</button> 
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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.baddbmm</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.baddbmm.html" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="JAX (Core)_1" class="op-tab-pane "><h4>JAX (Core)</h4><div class="op-detail-row"><span class="label">API:</span> <code>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro '{beta} * {input} + {alpha} * jax.numpy.matmul({batch1}, {batch2})'</span></div></div><div id="NumPy_2" class="op-tab-pane "><h4>NumPy</h4><div class="op-detail-row"><span class="label">API:</span> <code>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro '{beta} * {input} + {alpha} * numpy.matmul({batch1}, {batch2})'</span></div></div><div id="Keras_3" class="op-tab-pane "><h4>Keras</h4><div class="op-detail-row"><span class="label">API:</span> <code>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro '{beta} * {input} + {alpha} * keras.ops.matmul({batch1}, {batch2})'</span></div></div><div id="Apple MLX_4" class="op-tab-pane "><h4>Apple MLX</h4><div class="op-detail-row"><span class="label">API:</span> <code>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro '{beta} * {input} + {alpha} * mlx.core.matmul({batch1}, {batch2})'</span></div></div><div id="Flax NNX_5" class="op-tab-pane "><h4>Flax NNX</h4><div class="op-detail-row"><span class="label">API:</span> <code>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro '{beta} * {input} + {alpha} * jax.numpy.matmul({batch1}, {batch2})'</span></div></div> 
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