class_docstr: ## ResNet Block
docstr: :param x: is the input feature map with shape `[batch_size, channels, height, width]`
code: def forward(self, x: torch.Tensor):
      """
      :param x: is the input feature map with shape `[batch_size, channels, height, width]`
      """
  
      h = x
  
      # First normalization and convolution layer
      h = self.norm1(h)
      h = swish(h)
      h = self.conv1(h)
  
      # Second normalization and convolution layer
      h = self.norm2(h)
      h = swish(h)
      h = self.conv2(h)
  
      # Map and add residual
      return self.nin_shortcut(x) + h
  
imports: from typing import List
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the ResnetBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown