class_docstr: Module wrapper that returns intermediate layers from a model
  It has a strong assumption that the modules have been registered
  into the model in the same order as they are used.
  This means that one should **not** reuse the same nn.Module
  twice in the forward if you want this to work.
  Additionally, it is only able to query submodules that are directly
  assigned to the model. So if `model` is passed, `model.feature1` can
  be returned, but not `model.feature1.layer2`.
  Arguments:
      model (nn.Module): model on which we will extract the features
      return_layers (Dict[name, new_name]): a dict containing the names
          of the modules for which the activations will be returned as
          the key of the dict, and the value of the dict is the name
          of the returned activation (which the user can specify).
code: def __init__(self, model, return_layers):
      if not set(return_layers).issubset(
          [name for name, _ in model.named_children()]
      ):
          raise ValueError("return_layers are not present in model")
  
      orig_return_layers = return_layers
      return_layers = {str(k): str(v) for k, v in return_layers.items()}
      layers = OrderedDict()
  
      # 遍历模型子模块按顺序存入有序字典
      # 只保存layer4及其之前的结构，舍去之后不用的结构
      for name, module in model.named_children():
          layers[name] = module
          if name in return_layers:
              del return_layers[name]
          if not return_layers:
              break
  
      super().__init__(layers)
      self.return_layers = orig_return_layers
  
imports: from collections import OrderedDict
  import torch.nn
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.jit.annotations import Tuple, List, Dict
label_desc: Use image node_id 1 to create a new IntermediateLayerGetter object from inherited base classes: nn with example: obj = IntermediateLayerGetter(model, return_layers)