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: Select a new task to train on.
  
  If there is a fixed set of training tasks, this will typically cycle through them.
  If there are infinitely many training tasks, this can simply select a new one each
  time it is called.
docstr_tok: ["Select","a","new","task","to","train","on",".","If","there","is","a","fixed","set","of","training","tasks",",","this","will","typically","cycle","through","them",".","If","there","are","infinitely","many","training","tasks",",","this","can","simply","select","a","new","one","each","time","it","is","called","."]
code: def select_task(self):
      """Select a new task to train on.
  
      If there is a fixed set of training tasks, this will typically cycle through them.
      If there are infinitely many training tasks, this can simply select a new one each
      time it is called.
      """
      raise NotImplementedError("Subclasses must implement this")
  
file_path: deepchem/deepchem/metalearning/__init__.py
imports: import logging
label_desc: Use image node_id 3 for calling the MetaLearner obj's underlying member method code with example usage: obj.select_task() without return types