class_docstr: Model and data to which the MAML algorithm can be applied.
  
  To use MAML, create a subclass of this defining the learning problem to solve.
  It consists of a model that can be trained to perform many different tasks, and
  data for training it on a large (possibly infinite) set of different tasks.
docstr: Returns an iterator over the MetaLearner parameters.
docstr_tok: ["Returns","an","iterator","over","the","MetaLearner","parameters","."]
code: def parameters(self):
      """Returns an iterator over the MetaLearner parameters."""
      raise NotImplementedError("Subclasses must implement this")
  
file_path: deepchem/deepchem/metalearning/__init__.py
imports: import logging
label_desc: Use image node_id 5 for calling the MetaLearner obj's underlying member method code with example usage: obj.parameters() without return types