padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`,
            :math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`)

    Shape:
        - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
        - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where

          :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`

          :math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`

    Examples::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = nn.ConstantPad2d(2, 3.5)
        >>> input = torch.randn(1, 2, 2)
        >>> input
        tensor([[[ 1.6585,  0.4320],
                 [-0.8701, -0.4649]]])
        >>> m(input)
        tensor([[[ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  1.6585,  0.4320,  3.5000,  3.5000],
                 [ 3.5000,  3.5000, -0.8701, -0.4649,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000,  3.5000]]])
        >>> # using different paddings for different sides
        >>> m = nn.ConstantPad2d((3, 0, 2, 1), 3.5)
        >>> m(input)
        tensor([[[ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000],
                 [ 3.5000,  3.5000,  3.5000,  1.6585,  0.4320],
                 [ 3.5000,  3.5000,  3.5000, -0.8701, -0.4649],
                 [ 3.5000,  3.5000,  3.5000,  3.5000,  3.5000]]])

    r