class_docstr: A set of predefined models to approximate Q or log of pi (policy).
  
  The loss function needs to be 'cross_entropy_with_softmax' for policy
  gradient methods.
code: def _parse_dueling_network_structure(hidden_layers_str):
      hidden_layers = ast.literal_eval(hidden_layers_str)
  
      if not (
          len(hidden_layers) > 2
          and isinstance(hidden_layers[-1], list)
          and isinstance(hidden_layers[-2], list)
      ):
          raise ValueError("Invalid dueling network structure.")
  
      return (
          Models._remove_none_elements_from_list(hidden_layers[:-2]),
          Models._remove_none_elements_from_list(hidden_layers[-2]),
          Models._remove_none_elements_from_list(hidden_layers[-1]),
      )
  
file_path: CNTK/bindings/python/cntk/contrib/deeprl/agent/shared/models.py
imports: import cntk
  import numpy
  import ast
label_desc: Use image node_id 3 for calling the Models obj's underlying member method code with example usage: obj._parse_dueling_network_structure(hidden_layers_str) and returns: Models,  Models,  Models