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: Compute the model for a set of inputs and variables.
  
  Parameters
  ----------
  inputs: list of tensors
      the inputs to the model
  variables: list of tensors
      the values to use for the model's variables.  This might be the actual
      variables (as returned by the MetaLearner's variables property), or
      alternatively it might be the values of those variables after one or more
      steps of gradient descent for the current task.
  training: bool
      indicates whether the model is being invoked for training or prediction
  
  Returns
  -------
  (loss, outputs) where loss is the value of the model's loss function, and
  outputs is a list of the model's outputs
docstr_tok: ["Compute","the","model","for","a","set","of","inputs","and","variables",".","Parameters","--","--","--","--","--","inputs",":","list","of","tensors","the","inputs","to","the","model","variables",":","list","of","tensors","the","values","to","use","for","the","model","'s","variables",".","This","might","be","the","actual","variables","(","as","returned","by","the","MetaLearner","'s","variables","property",")",",","or","alternatively","it","might","be","the","values","of","those","variables","after","one","or","more","steps","of","gradient","descent","for","the","current","task",".","training",":","bool","indicates","whether","the","model","is","being","invoked","for","training","or","prediction","Returns","--","--","--","-","(","loss",",","outputs",")","where","loss","is","the","value","of","the","model","'s","loss","function",",","and","outputs","is","a","list","of","the","model","'s","outputs"]
code: def compute_model(self, inputs, variables, training):
      """Compute the model for a set of inputs and variables.
  
      Parameters
      ----------
      inputs: list of tensors
          the inputs to the model
      variables: list of tensors
          the values to use for the model's variables.  This might be the actual
          variables (as returned by the MetaLearner's variables property), or
          alternatively it might be the values of those variables after one or more
          steps of gradient descent for the current task.
      training: bool
          indicates whether the model is being invoked for training or prediction
  
      Returns
      -------
      (loss, outputs) where loss is the value of the model's loss function, and
      outputs is a list of the model's outputs
      """
      raise NotImplementedError("Subclasses must implement this")
  
file_path: deepchem/deepchem/metalearning/__init__.py
imports: import logging
label_desc: Use image node_id 1 for calling the MetaLearner obj's underlying member method code with example usage: obj.compute_model(inputs, variables, training) without return types