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# ConstantPad3d

class torch.nn.ConstantPad3d(padding, value)

  Pads the input tensor boundaries with a constant value.

  For N-dimensional padding, use `torch.nn.functional.pad()`.

  Parameters:

    padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 6-tuple, uses (padding_left\text{padding\_left}padding_left, padding_right\text{padding\_right}padding_right, padding_top\text{padding\_top}padding_top, padding_bottom\text{padding\_bottom}padding_bottom, padding_front\text{padding\_front}padding_front, padding_back\text{padding\_back}padding_back)

  Shape:

    - Input: (N,C,D i n,H i n,W i n)(N, C, D_{in}, H_{in}, W_{in})(N,C,D in​,H in​,W in​) or (C,D i n,H i n,W i n)(C, D_{in}, H_{in}, W_{in})(C,D in​,H in​,W in​).

    - Output: (N,C,D o u t,H o u t,W o u t)(N, C, D_{out}, H_{out}, W_{out})(N,C,D o u t​,H o u t​,W o u t​) or (C,D o u t,H o u t,W o u t)(C, D_{out}, H_{out}, W_{out})(C,D o u t​,H o u t​,W o u t​), where

      D o u t=D i n+padding_front+padding_back D_{out} = D_{in} + \text{padding\_front} + \text{padding\_back}D o u t​=D in​+padding_front+padding_back

      H o u t=H i n+padding_top+padding_bottom H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}H o u t​=H in​+padding_top+padding_bottom

      W o u t=W i n+padding_left+padding_right W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}W o u t​=W in​+padding_left+padding_right

  Examples:

      >>> m = nn.ConstantPad3d(3, 3.5)
      >>> input = torch.randn(16, 3, 10, 20, 30)
      >>> output = m(input)
      >>> # using different paddings for different sides
      >>> m = nn.ConstantPad3d((3, 3, 6, 6, 0, 1), 3.5)
      >>> output = m(input)

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