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# LazyConvTranspose2d

class torch.nn.LazyConvTranspose2d(out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None)

  A `torch.nn.ConvTranspose2d` module with lazy initialization of the `in_channels` argument of the `ConvTranspose2d` that is inferred from the `input.size(1)`. The attributes that will be lazily initialized are weight and bias.

  Check the `torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations.

  Parameters:

    - out_channels (int) – Number of channels produced by the convolution

    - kernel_size (int or tuple) – Size of the convolving kernel

    - stride (int or tuple, optional) – Stride of the convolution. Default: 1

    - padding (int or tuple, optional) – `dilation * (kernel_size - 1) - padding` zero-padding will be added to both sides of each dimension in the input. Default: 0

    - output_padding (int or tuple, optional) – Additional size added to one side of each dimension in the output shape. Default: 0

    - groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1

    - bias (bool, optional) – If `True`, adds a learnable bias to the output. Default: `True`

    - dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1

  See also

  `torch.nn.ConvTranspose2d` and `torch.nn.modules.lazy.LazyModuleMixin`

  cls_to_become

    alias of `ConvTranspose2d`

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