code: def forward(self, x: torch.Tensor):
      x = self.z_score(x)
  
      output = []
      for i, (_, layer) in enumerate(self.layers._modules.items(), 1):
          x = layer(x)
          if i in self.target_layers:
              output.append(normalize_activation(x))
          if len(output) == len(self.target_layers):
              break
      return output
  
imports: from typing import Sequence
  from itertools import chain
  import torch
  import torch.nn
  from torchvision import models
  from .utils import normalize_activation
label_desc: Use image node_id 4 for calling the BaseNet obj's underlying member method code with example usage: obj.forward(x) and returns: output