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: Get a batch of data for training.
  
  This should return the data as a list of arrays, one for each of the model's
  inputs.  This will usually be called twice for each task, and should
  return a different batch on each call.
docstr_tok: ["Get","a","batch","of","data","for","training",".","This","should","return","the","data","as","a","list","of","arrays",",","one","for","each","of","the","model's","inputs",".","This","will","usually","be","called","twice","for","each","task",",","and","should","return","a","different","batch","on","each","call","."]
code: def get_batch(self):
      """Get a batch of data for training.
  
      This should return the data as a list of arrays, one for each of the model's
      inputs.  This will usually be called twice for each task, and should
      return a different batch on each call.
      """
      raise NotImplementedError("Subclasses must implement this")
  
file_path: deepchem/deepchem/metalearning/__init__.py
imports: import logging
label_desc: Use image node_id 4 for calling the MetaLearner obj's underlying member method code with example usage: obj.get_batch() without return types