class_docstr: The interface of a mixin class.
docstr: Build a model from a list of keywords.
  
  A BackendEngine should implement get_model in order to
  simplify the user's work. In particular, we will adhere
  to the following API where list_nodes is a list of neural network
  layers.
  
  Parameters
  ----------
  list_nodes: list
      list of keywords.
docstr_tok: ["Build","a","model","from","a","list","of","keywords",".","A","BackendEngine","should","implement","get_model","in","order","to","simplify","the","user","'s","work",".","In","particular",",","we","will","adhere","to","the","following","API","where","list_nodes","is","a","list","of","neural","network","layers",".","Parameters","--","--","--","--","--","list_nodes",":","list","list","of","keywords","."]
code: def get_model(self, list_nodes):
      """
      Build a model from a list of keywords.
  
      A BackendEngine should implement get_model in order to
      simplify the user's work. In particular, we will adhere
      to the following API where list_nodes is a list of neural network
      layers.
  
      Parameters
      ----------
      list_nodes: list
          list of keywords.
      """
      raise NotImplementedError(_NOT_IMPLEMENTED)
  
file_path: fairlearn/fairlearn/adversarial/_backend_engine.py
imports: from ._constants import _KWARG_ERROR_MESSAGE, _NOT_IMPLEMENTED, _DIST_TYPE_NOT_IMPLEMENTED, _NO_LOSS, _LIST_MODEL_UNSUPPORTED
  from sklearn.utils import shuffle
  from numpy import ndarray
label_desc: Use image node_id 10 for calling the BackendEngine obj's underlying member method code with example usage: obj.get_model(list_nodes) without return types