class_docstr: Construct 3d max pool with same padding. PyTorch does not provide same padding.
  Same padding means the output size matches input size for stride=1.
class_docstr_tok: ["Construct","3d","max","pool","with","same","padding",".","PyTorch","does","not","provide","same","padding",".","Same","padding","means","the","output","size","matches","input","size","for","stride=1","."]
docstr: Compute 'same' padding. Add zero to the back position first.
docstr_tok: ["Compute","'same","'","padding",".","Add","zero","to","the","back","position","first","."]
code: def forward(self, x):
      """Compute 'same' padding. Add zero to the back position first."""
  
      (batch, channel, time, height, width) = x.size()
      pad_t = self.compute_pad(0, time)
      pad_h = self.compute_pad(1, height)
      pad_w = self.compute_pad(2, width)
  
      pad_t_front = pad_t // 2
      pad_t_back = pad_t - pad_t_front
      pad_h_front = pad_h // 2
      pad_h_back = pad_h - pad_h_front
      pad_w_front = pad_w // 2
      pad_w_back = pad_w - pad_w_front
  
      pad = (
          pad_w_front,
          pad_w_back,
          pad_h_front,
          pad_h_back,
          pad_t_front,
          pad_t_back,
      )
      x = F.pad(x, pad)
      return super(MaxPool3dSamePadding, self).forward(x)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 2 for calling the MaxPool3dSamePadding obj's underlying member method code with example usage: obj.forward(x) and returns: super