code: def forward(self, x) -> List[torch.Tensor]:
      for module in self:
          x = module(x)
      return x
  
imports: import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg, pretrained_cfg_for_features
  from ._features import FeatureInfo
  from ._registry import register_model, generate_default_cfgs
  from .resnet import BasicBlock, Bottleneck
label_desc: Use image node_id 4 for calling the SequentialList obj's underlying member method code with example usage: obj.forward(x) and returns: x