code: def get_model(name):
      """Returns a model instance such as `model = vision_models.InceptionV1()`.
      In the future may be expanded to filter by additional criteria, such as
      architecture, dataset, and task the model was trained on.
      Args:
        name: The name of the model, as given by the class name in vision_models.
      Returns:
        An instantiated Model class with the requested model. Users still need to
        manually `load_graphdef` on the return value, and manually import this
        model's graph into their current graph.
      Raises:
        ValueError: If network `name` is not recognized.
      """
      if name not in models_map:
          candidates = filter(
              lambda key: name in key, models_map.keys()
          )
          candidates_string = ", ".join(candidates)
          raise ValueError(
              "No network named {}. Did you mean one of {}?".format(
                  name, candidates_string
              )
          )
  
      model_class = models_map[name]
      model = model_class()
      return model
  
imports: from __future__ import absolute_import, division, print_function
  import inspect
  import tensorflow
  from lucid.modelzoo import vision_models
  from lucid.modelzoo import vision_base
label_desc: Use image node_id 2 for calling a global function with example usage: get_model(name) and returns: model