code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      if self.input_size != self.output_size:
          x = self.resample(x)
  
      if self.trainable_add:
          x = x * self.gate(self.mask) * 2.0
  
      output = self.norm(x)
      return output
  
imports: import math
  from typing import Dict, List, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the ResampleNorm obj's underlying member method code with example usage: obj.forward(x) and returns: output