class_docstr: Model-Agnostic Meta-Learning (MAML).
  
  Args:
      inner_algo (garage.torch.algos.VPG): The inner algorithm used for
          computing loss.
      env (Environment): An environment.
      policy (garage.torch.policies.Policy): Policy.
      sampler (garage.sampler.Sampler): Sampler.
      task_sampler (garage.experiment.TaskSampler): Task sampler.
      meta_optimizer (Union[torch.optim.Optimizer, tuple]):
          Type of optimizer.
          This can be an optimizer type such as `torch.optim.Adam` or a tuple
          of type and dictionary, where dictionary contains arguments to
          initialize the optimizer e.g. `(torch.optim.Adam, {'lr' : 1e-3})`.
      meta_batch_size (int): Number of tasks sampled per batch.
      inner_lr (float): Adaptation learning rate.
      outer_lr (float): Meta policy learning rate.
      num_grad_updates (int): Number of adaptation gradient steps.
      meta_evaluator (MetaEvaluator): A meta evaluator for meta-testing. If
          None, don't do meta-testing.
      evaluate_every_n_epochs (int): Do meta-testing every this epochs.
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docstr: Obtain samples and start training for each epoch.
  
  Args:
      trainer (Trainer): Gives the algorithm access to
          :method:`~Trainer.step_epochs()`, which provides services
          such as snapshotting and sampler control.
  
  Returns:
      float: The average return in last epoch cycle.
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code: def train(self, trainer):
      """Obtain samples and start training for each epoch.
  
      Args:
          trainer (Trainer): Gives the algorithm access to
              :method:`~Trainer.step_epochs()`, which provides services
              such as snapshotting and sampler control.
  
      Returns:
          float: The average return in last epoch cycle.
  
      """
      last_return = None
  
      for _ in trainer.step_epochs():
          all_samples, all_params = self._obtain_samples(trainer)
          last_return = self._train_once(
              trainer, all_samples, all_params
          )
          trainer.step_itr += 1
  
      return last_return
  
imports: import collections
  import copy
  from dowel import tabular
  import numpy
  import torch
  from garage import _Default, EpisodeBatch, log_multitask_performance, make_optimizer
  from garage.np import discount_cumsum
  from garage.torch import update_module_params
  from garage.torch._functions import np_to_torch, zero_optim_grads
  from garage.torch.optimizers import ConjugateGradientOptimizer, DifferentiableSGD
label_desc: Use image node_id 2 for calling the MAML obj's underlying member method code with example usage: obj.train(trainer) and returns: last_return