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The torch.nn.attention.bias module contains attention_biases that are designed to be used with scaled_dot_product_attention.

Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called "deconvolution".

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Applies 3D average-pooling operation in kT×kH×kWkT \times kH \times kWkT×kH×kW regions by step size sT×sH×sWsT \times sH \times sWsT×sH×sW steps.

Takes LongTensor with index values of shape (*) and returns a tensor of shape (*, num_classes) that have zeros everywhere except where the index of last dimension matches the corresponding value of the input tensor, in which case it will be 1.

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© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation. For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see www.linuxfoundation.org/policies/. The PyTorch Foundation supports the PyTorch open source project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC, please see www.lfprojects.org/policies/.

Applies element-wise, SELU(x)=scale∗(max⁡(0,x)+min⁡(0,α∗(exp⁡(x)−1)))\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))SELU(x)=scale∗(max(0,x)+min(0,α∗(exp(x)−1))), with α=1.6732632423543772848170429916717\alpha=1.6732632423543772848170429916717α=1.6732632423543772848170429916717 and scale=1.0507009873554804934193349852946scale=1.0507009873554804934193349852946scale=1.0507009873554804934193349852946.

Rearranges elements in a tensor of shape (∗,C×r2,H,W)(*, C \times r^2, H, W)(∗,C×r2,H,W) to a tensor of shape (∗,C,H×r,W×r)(*, C, H \times r, W \times r)(∗,C,H×r,W×r), where r is the upscale_factor.

Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called "deconvolution".

Applies element-wise LogSigmoid(xi)=log⁡(11+exp⁡(−xi))\text{LogSigmoid}(x_i) = \log \left(\frac{1}{1 + \exp(-x_i)}\right)LogSigmoid(xi​)=log(1+exp(−xi​)1​)

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When the approximate argument is 'none', it applies element-wise the function GELU(x)=x∗Φ(x)\text{GELU}(x) = x * \Phi(x)GELU(x)=x∗Φ(x)

Applies element-wise, the function Softplus(x)=1β∗log⁡(1+exp⁡(β∗x))\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))Softplus(x)=β1​∗log(1+exp(β∗x)).

Applies element-wise the function PReLU(x)=max⁡(0,x)+weight∗min⁡(0,x)\text{PReLU}(x) = \max(0,x) + \text{weight} * \min(0,x)PReLU(x)=max(0,x)+weight∗min(0,x) where weight is a learnable parameter.

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Applies element-wise, CELU(x)=max⁡(0,x)+min⁡(0,α∗(exp⁡(x/α)−1))\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))CELU(x)=max(0,x)+min(0,α∗(exp(x/α)−1)).

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Reverses the PixelShuffle operation by rearranging elements in a tensor of shape (∗,C,H×r,W×r)(*, C, H \times r, W \times r)(∗,C,H×r,W×r) to a tensor of shape (∗,C×r2,H,W)(*, C \times r^2, H, W)(∗,C×r2,H,W), where r is the downscale_factor.

Applies a 3D transposed convolution operator over an input image composed of several input planes, sometimes also called "deconvolution"

Applies element-wise, LeakyReLU(x)=max⁡(0,x)+negative_slope∗min⁡(0,x)\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)LeakyReLU(x)=max(0,x)+negative_slope∗min(0,x)

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Applies element-wise, Tanh(x)=tanh⁡(x)=exp⁡(x)−exp⁡(−x)exp⁡(x)+exp⁡(−x)\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}Tanh(x)=tanh(x)=exp(x)+exp(−x)exp(x)−exp(−x)​

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