class_docstr: A wrapper class to handle torch.optim.optimizer.
  
  Args:
      optimizer (Union[type, tuple[type, dict]]): Type of optimizer
          for policy. 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})`
          Sample strategy to be used when sampling a new task.
      module (torch.nn.Module): Module to be optimized.
      max_optimization_epochs (int): Maximum number of epochs for update.
      minibatch_size (int): Batch size for optimization.
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docstr: Performs a single optimization step.
  
  Arguments:
      **closure (callable, optional): A closure that reevaluates the
          model and returns the loss.
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code: def step(self, **closure):
      """Performs a single optimization step.
  
      Arguments:
          **closure (callable, optional): A closure that reevaluates the
              model and returns the loss.
  
      """
      self._optimizer.step(**closure)
  
imports: from garage import make_optimizer
  from garage.np.optimizers import BatchDataset
label_desc: Use image node_id 4 for calling the OptimizerWrapper obj's underlying member method code with example usage: obj.step() without return types