single ``int`` -- in which case the same value is used for the height and width dimensions
        - a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension,
          and the second `int` for the width dimension

    Note:
        The :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding``
        amount of zero padding to both sizes of the input. This is set so that
        when a :class:`~torch.nn.Conv2d` and a :class:`~torch.nn.ConvTranspose2d`
        are initialized with same parameters, they are inverses of each other in
        regard to the input and output shapes. However, when ``stride > 1``,
        :class:`~torch.nn.Conv2d` maps multiple input shapes to the same output
        shape. :attr:`output_padding` is provided to resolve this ambiguity by
        effectively increasing the calculated output shape on one side. Note
        that :attr:`output_padding` is only used to find output shape, but does
        not actually add zero-padding to output.

    Note:
        {cudnn_reproducibility_note}

    Args:
        in_channels (int): Number of channels in the input image
        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
    a