class_docstr: Inline Conv2D for GroupNorm module
class_docstr_tok: ["Inline","Conv2D","for","GroupNorm","module"]
code: def forward(self, x):
      if self.padding == "same":
          height, width = x.shape[2:]
  
          h_pad_need = max(
              0, (height - 1) * self.stride + self.h_kernel - height
          )
          w_pad_need = max(
              0, (width - 1) * self.stride + self.w_kernel - width
          )
  
          pad_left = w_pad_need // 2
          pad_right = w_pad_need - pad_left
          pad_top = h_pad_need // 2
          pad_bottom = h_pad_need - pad_top
  
          padding = (pad_left, pad_right, pad_top, pad_bottom)
  
          x = F.pad(x, padding, "constant", 0)
  
      x = self.conv(x)
  
      return x
  
file_path: gluon-cv/gluoncv/torch/nn/group_norm.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Module, Parameter
  from torch.nn import init
label_desc: Use image node_id 2 for calling the Conv2D obj's underlying member method code with example usage: obj.forward(x) and returns: x