GeneralConv
===========

General convolution over an input with several channels.

**Abstract Signature:**

``GeneralConv(input: Tensor, weight: Tensor, stride: Union[int, Sequence[int]] = 1, padding: Union[int, Sequence[int]] = 0, kernel_dilation: Union[int, Sequence[int]] = 1, input_dilation: Union[int, Sequence[int]] = 1, groups: int = 1, flip: bool = False)``

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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>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Custom / Partial</span></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>jax.lax.conv_general_dilated</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Plugin (jax_conv_general_adapter)</span></div></div><div id="Apple MLX_2" class="op-tab-pane "><h4>Apple MLX</h4><div class="op-detail-row"><span class="label">API:</span> <code>mlx.core.conv_general</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://ml-explore.github.io/mlx/build/html/python/_autosummary/mlx.core.conv_general.html" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="Flax NNX_3" class="op-tab-pane "><h4>Flax NNX</h4><div class="op-detail-row"><span class="label">API:</span> <code>jax.lax.conv_general_dilated</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Plugin (jax_conv_general_adapter)</span></div></div><div id="PaxML / Praxis_4" class="op-tab-pane "><h4>PaxML / Praxis</h4><div class="op-detail-row"><span class="label">API:</span> <code>jax.lax.conv_general_dilated</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Plugin (jax_conv_general_adapter)</span></div></div> 
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