ReplicationPad2d
class torch.nn.ReplicationPad2d(padding)
Pads the input tensor using replication of the input boundary.
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 4-tuple,
      uses (\text{padding_left}, \text{padding_right},
      \text{padding_top}, \text{padding_bottom})
Shape:
      * Input: (N, C, H_{in}, W_{in}) or (C, H_{in}, W_{in}).
  * Output: (N, C, H_{out}, W_{out}) or (C, H_{out}, W_{out}),
    where

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

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

Examples:
  >>> m = nn.ReplicationPad2d(2)
  >>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3)
  >>> input
  tensor([[[[0., 1., 2.],
            [3., 4., 5.],
            [6., 7., 8.]]]])
