code: def _create_model_fn(model_name):
      def _model_fn(
          num_classes=1000, in_channels=3, pretrained="imagenet"
      ):
          """Create Efficient Net.
  
          Described in detail here: https://arxiv.org/abs/1905.11946
  
          Args:
              num_classes (int, optional): Number of classes, default is 1000.
              in_channels (int, optional): Number of input channels, default
                  is 3.
              pretrained (str, optional): One of [None, 'imagenet', 'advprop']
                  If None, no pretrained model is loaded.
                  If 'imagenet', models trained on imagenet dataset are loaded.
                  If 'advprop', models trained using adversarial training called
                  advprop are loaded. It is important to note that the
                  preprocessing required for the advprop pretrained models is
                  slightly different from normal ImageNet preprocessing
          """
          model_name_ = model_name.replace("_", "-")
          if pretrained is not None:
              model = _EfficientNet.from_pretrained(
                  model_name=model_name_,
                  advprop=(pretrained == "advprop"),
                  num_classes=num_classes,
                  in_channels=in_channels,
              )
          else:
              model = _EfficientNet.from_name(
                  model_name=model_name_,
                  override_params={"num_classes": num_classes},
              )
              model._change_in_channels(in_channels)
  
          return model
  
      return _model_fn
  
label_desc: Use image node_id 1 for calling a global function with example usage: _create_model_fn(model_name) and returns: _model_fn, model