class_docstr: IntermediateLayerGetter borrowed from torchvision
  
  Args:
      model (`nn.Module`):
          model on which we will extract the features.
      return_layers(`Dict`):
          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).
class_docstr_tok: ["IntermediateLayerGetter","borrowed","from","torchvision","Args",":","model","(","`","nn.Module","`",")",":","model","on","which","we","will","extract","the","features",".","return_layers","(","`","Dict","`",")",":","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 forward(self, x):
      out = OrderedDict()
      for name, module in self.items():
          x = module(x)
          if name in self.return_layers:
              out_name = self.return_layers[name]
              out[out_name] = x
      return out
  
file_path: towhee/towhee/models/retina_face/utils.py
imports: from typing import Dict
  from collections import OrderedDict
  import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the IntermediateLayerGetter obj's underlying member method code with example usage: obj.forward(x) and returns: out