ZeroPad3d# class torch.nn.ZeroPad3d(padding)[source]# Pads the input tensor boundaries with zero. 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,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in})(N,C,Din,Hin,Win) or (C,Din,Hin,Win)(C, D_{in}, H_{in}, W_{in})(C,Din,Hin,Win). Output: (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out})(N,C,Dout,Hout,Wout) or (C,Dout,Hout,Wout)(C, D_{out}, H_{out}, W_{out})(C,Dout,Hout,Wout), where Dout=Din+padding_front+padding_backD_{out} = D_{in} + \text{padding\_front} + \text{padding\_back}Dout=Din+padding_front+padding_back Hout=Hin+padding_top+padding_bottomH_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}Hout=Hin+padding_top+padding_bottom Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}Wout=Win+padding_left+padding_right Examples: >>> m = nn.ZeroPad3d(3) >>> input = torch.randn(16, 3, 10, 20, 30) >>> output = m(input) >>> # using different paddings for different sides >>> m = nn.ZeroPad3d((3, 3, 6, 6, 0, 1)) >>> output = m(input) extra_repr()[source]# Return the extra representation of the module. Return type str