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<li class="toctree-l1 has-children"><a class="reference internal" href="../linalg.html">Linear Algebra</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l1 current active has-children"><a class="reference internal" href="../nn.html">Neural Networks</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
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<li class="toctree-l2 current active has-children"><a class="current reference internal" href="#">Layers</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2 has-children"><a class="reference internal" href="functions.html">Functions</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="_autosummary_functions/mlx.nn.elu.html">mlx.nn.elu</a></li>
<li class="toctree-l3"><a class="reference internal" href="_autosummary_functions/mlx.nn.celu.html">mlx.nn.celu</a></li>
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<h1>Layers</h1>
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<section id="layers">
<span id="id1"></span><h1>Layers<a class="headerlink" href="#layers" title="Link to this heading">#</a></h1>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ALiBi.html#mlx.nn.ALiBi" title="mlx.nn.ALiBi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ALiBi</span></code></a>()</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.AvgPool1d.html#mlx.nn.AvgPool1d" title="mlx.nn.AvgPool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AvgPool1d</span></code></a>(kernel_size[, stride, padding])</p></td>
<td><p>Applies 1-dimensional average pooling.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.AvgPool2d.html#mlx.nn.AvgPool2d" title="mlx.nn.AvgPool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AvgPool2d</span></code></a>(kernel_size[, stride, padding])</p></td>
<td><p>Applies 2-dimensional average pooling.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.AvgPool3d.html#mlx.nn.AvgPool3d" title="mlx.nn.AvgPool3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AvgPool3d</span></code></a>(kernel_size[, stride, padding])</p></td>
<td><p>Applies 3-dimensional average pooling.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.BatchNorm.html#mlx.nn.BatchNorm" title="mlx.nn.BatchNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BatchNorm</span></code></a>(num_features[, eps, momentum, ...])</p></td>
<td><p>Applies Batch Normalization over a 2D or 3D input.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.CELU.html#mlx.nn.CELU" title="mlx.nn.CELU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CELU</span></code></a>([alpha])</p></td>
<td><p>Applies the Continuously Differentiable Exponential Linear Unit.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Conv1d.html#mlx.nn.Conv1d" title="mlx.nn.Conv1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv1d</span></code></a>(in_channels, out_channels, kernel_size)</p></td>
<td><p>Applies a 1-dimensional convolution over the multi-channel input sequence.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Conv2d.html#mlx.nn.Conv2d" title="mlx.nn.Conv2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv2d</span></code></a>(in_channels, out_channels, kernel_size)</p></td>
<td><p>Applies a 2-dimensional convolution over the multi-channel input image.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Conv3d.html#mlx.nn.Conv3d" title="mlx.nn.Conv3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv3d</span></code></a>(in_channels, out_channels, kernel_size)</p></td>
<td><p>Applies a 3-dimensional convolution over the multi-channel input image.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose1d.html#mlx.nn.ConvTranspose1d" title="mlx.nn.ConvTranspose1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvTranspose1d</span></code></a>(in_channels, out_channels, ...)</p></td>
<td><p>Applies a 1-dimensional transposed convolution over the multi-channel input sequence.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose2d.html#mlx.nn.ConvTranspose2d" title="mlx.nn.ConvTranspose2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvTranspose2d</span></code></a>(in_channels, out_channels, ...)</p></td>
<td><p>Applies a 2-dimensional transposed convolution over the multi-channel input image.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose3d.html#mlx.nn.ConvTranspose3d" title="mlx.nn.ConvTranspose3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvTranspose3d</span></code></a>(in_channels, out_channels, ...)</p></td>
<td><p>Applies a 3-dimensional transposed convolution over the multi-channel input image.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Dropout.html#mlx.nn.Dropout" title="mlx.nn.Dropout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dropout</span></code></a>([p])</p></td>
<td><p>Randomly zero a portion of the elements during training.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Dropout2d.html#mlx.nn.Dropout2d" title="mlx.nn.Dropout2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dropout2d</span></code></a>([p])</p></td>
<td><p>Apply 2D channel-wise dropout during training.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Dropout3d.html#mlx.nn.Dropout3d" title="mlx.nn.Dropout3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dropout3d</span></code></a>([p])</p></td>
<td><p>Apply 3D channel-wise dropout during training.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Embedding.html#mlx.nn.Embedding" title="mlx.nn.Embedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Embedding</span></code></a>(num_embeddings, dims)</p></td>
<td><p>Implements a simple lookup table that maps each input integer to a high-dimensional vector.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ELU.html#mlx.nn.ELU" title="mlx.nn.ELU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ELU</span></code></a>([alpha])</p></td>
<td><p>Applies the Exponential Linear Unit.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.GELU.html#mlx.nn.GELU" title="mlx.nn.GELU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GELU</span></code></a>([approx])</p></td>
<td><p>Applies the Gaussian Error Linear Units.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.GLU.html#mlx.nn.GLU" title="mlx.nn.GLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GLU</span></code></a>([axis])</p></td>
<td><p>Applies the gated linear unit function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.GroupNorm.html#mlx.nn.GroupNorm" title="mlx.nn.GroupNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GroupNorm</span></code></a>(num_groups, dims[, eps, affine, ...])</p></td>
<td><p>Applies Group Normalization [1] to the inputs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.GRU.html#mlx.nn.GRU" title="mlx.nn.GRU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GRU</span></code></a>(input_size, hidden_size[, bias])</p></td>
<td><p>A gated recurrent unit (GRU) RNN layer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.HardShrink.html#mlx.nn.HardShrink" title="mlx.nn.HardShrink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HardShrink</span></code></a>()</p></td>
<td><p>Applies the HardShrink function.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.HardTanh.html#mlx.nn.HardTanh" title="mlx.nn.HardTanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HardTanh</span></code></a>()</p></td>
<td><p>Applies the HardTanh function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Hardswish.html#mlx.nn.Hardswish" title="mlx.nn.Hardswish"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Hardswish</span></code></a>()</p></td>
<td><p>Applies the hardswish function, element-wise.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.InstanceNorm.html#mlx.nn.InstanceNorm" title="mlx.nn.InstanceNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InstanceNorm</span></code></a>(dims[, eps, affine])</p></td>
<td><p>Applies instance normalization [1] on the inputs.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LayerNorm.html#mlx.nn.LayerNorm" title="mlx.nn.LayerNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LayerNorm</span></code></a>(dims[, eps, affine, bias])</p></td>
<td><p>Applies layer normalization [1] on the inputs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LeakyReLU.html#mlx.nn.LeakyReLU" title="mlx.nn.LeakyReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LeakyReLU</span></code></a>([negative_slope])</p></td>
<td><p>Applies the Leaky Rectified Linear Unit.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Linear.html#mlx.nn.Linear" title="mlx.nn.Linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Linear</span></code></a>(input_dims, output_dims[, bias])</p></td>
<td><p>Applies an affine transformation to the input.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LogSigmoid.html#mlx.nn.LogSigmoid" title="mlx.nn.LogSigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogSigmoid</span></code></a>()</p></td>
<td><p>Applies the Log Sigmoid function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LogSoftmax.html#mlx.nn.LogSoftmax" title="mlx.nn.LogSoftmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogSoftmax</span></code></a>()</p></td>
<td><p>Applies the Log Softmax function.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LSTM.html#mlx.nn.LSTM" title="mlx.nn.LSTM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LSTM</span></code></a>(input_size, hidden_size[, bias])</p></td>
<td><p>An LSTM recurrent layer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MaxPool1d.html#mlx.nn.MaxPool1d" title="mlx.nn.MaxPool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a>(kernel_size[, stride, padding])</p></td>
<td><p>Applies 1-dimensional max pooling.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MaxPool2d.html#mlx.nn.MaxPool2d" title="mlx.nn.MaxPool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a>(kernel_size[, stride, padding])</p></td>
<td><p>Applies 2-dimensional max pooling.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MaxPool3d.html#mlx.nn.MaxPool3d" title="mlx.nn.MaxPool3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaxPool3d</span></code></a>(kernel_size[, stride, padding])</p></td>
<td><p>Applies 3-dimensional max pooling.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Mish.html#mlx.nn.Mish" title="mlx.nn.Mish"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Mish</span></code></a>()</p></td>
<td><p>Applies the Mish function, element-wise.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MultiHeadAttention.html#mlx.nn.MultiHeadAttention" title="mlx.nn.MultiHeadAttention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiHeadAttention</span></code></a>(dims, num_heads[, ...])</p></td>
<td><p>Implements the scaled dot product attention with multiple heads.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.PReLU.html#mlx.nn.PReLU" title="mlx.nn.PReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PReLU</span></code></a>([num_parameters, init])</p></td>
<td><p>Applies the element-wise parametric ReLU.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.QuantizedEmbedding.html#mlx.nn.QuantizedEmbedding" title="mlx.nn.QuantizedEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantizedEmbedding</span></code></a>(num_embeddings, dims[, ...])</p></td>
<td><p>The same as <a class="reference internal" href="_autosummary/mlx.nn.Embedding.html#mlx.nn.Embedding" title="mlx.nn.Embedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Embedding</span></code></a> but with a quantized weight matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.QuantizedLinear.html#mlx.nn.QuantizedLinear" title="mlx.nn.QuantizedLinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantizedLinear</span></code></a>(input_dims, output_dims[, ...])</p></td>
<td><p>Applies an affine transformation to the input using a quantized weight matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.RMSNorm.html#mlx.nn.RMSNorm" title="mlx.nn.RMSNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RMSNorm</span></code></a>(dims[, eps])</p></td>
<td><p>Applies Root Mean Square normalization [1] to the inputs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ReLU.html#mlx.nn.ReLU" title="mlx.nn.ReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReLU</span></code></a>()</p></td>
<td><p>Applies the Rectified Linear Unit.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ReLU6.html#mlx.nn.ReLU6" title="mlx.nn.ReLU6"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReLU6</span></code></a>()</p></td>
<td><p>Applies the Rectified Linear Unit 6.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.RNN.html#mlx.nn.RNN" title="mlx.nn.RNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RNN</span></code></a>(input_size, hidden_size[, bias, ...])</p></td>
<td><p>An Elman recurrent layer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.RoPE.html#mlx.nn.RoPE" title="mlx.nn.RoPE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RoPE</span></code></a>(dims[, traditional, base, scale])</p></td>
<td><p>Implements the rotary positional encoding.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.SELU.html#mlx.nn.SELU" title="mlx.nn.SELU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SELU</span></code></a>()</p></td>
<td><p>Applies the Scaled Exponential Linear Unit.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Sequential.html#mlx.nn.Sequential" title="mlx.nn.Sequential"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sequential</span></code></a>(*modules)</p></td>
<td><p>A layer that calls the passed callables in order.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Sigmoid.html#mlx.nn.Sigmoid" title="mlx.nn.Sigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sigmoid</span></code></a>()</p></td>
<td><p>Applies the sigmoid function, element-wise.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.SiLU.html#mlx.nn.SiLU" title="mlx.nn.SiLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SiLU</span></code></a>()</p></td>
<td><p>Applies the Sigmoid Linear Unit.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.SinusoidalPositionalEncoding.html#mlx.nn.SinusoidalPositionalEncoding" title="mlx.nn.SinusoidalPositionalEncoding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SinusoidalPositionalEncoding</span></code></a>(dims[, ...])</p></td>
<td><p>Implements sinusoidal positional encoding.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softmin.html#mlx.nn.Softmin" title="mlx.nn.Softmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softmin</span></code></a>()</p></td>
<td><p>Applies the Softmin function.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softshrink.html#mlx.nn.Softshrink" title="mlx.nn.Softshrink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softshrink</span></code></a>([lambd])</p></td>
<td><p>Applies the Softshrink function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softsign.html#mlx.nn.Softsign" title="mlx.nn.Softsign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softsign</span></code></a>()</p></td>
<td><p>Applies the Softsign function.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softmax.html#mlx.nn.Softmax" title="mlx.nn.Softmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softmax</span></code></a>()</p></td>
<td><p>Applies the Softmax function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softplus.html#mlx.nn.Softplus" title="mlx.nn.Softplus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softplus</span></code></a>()</p></td>
<td><p>Applies the Softplus function.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Step.html#mlx.nn.Step" title="mlx.nn.Step"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Step</span></code></a>([threshold])</p></td>
<td><p>Applies the Step Activation Function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Tanh.html#mlx.nn.Tanh" title="mlx.nn.Tanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tanh</span></code></a>()</p></td>
<td><p>Applies the hyperbolic tangent function.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Transformer.html#mlx.nn.Transformer" title="mlx.nn.Transformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transformer</span></code></a>(dims, num_heads, ...)</p></td>
<td><p>Implements a standard Transformer model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Upsample.html#mlx.nn.Upsample" title="mlx.nn.Upsample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Upsample</span></code></a>(scale_factor[, mode, align_corners])</p></td>
<td><p>Upsample the input signal spatially.</p></td>
</tr>
</tbody>
</table>
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