class_docstr: Environment form of a MetaWorld benchmark.
  
  This class is generally less efficient than using a TaskSampler, if that
  can be used instead, since each instance of this class internally caches a
  copy of each environment in the benchmark.
  
  In order to sample tasks from this environment, a benchmark must be passed
  at construction time.
  
  Args:
      benchmark (metaworld.Benchmark or None): The benchmark to wrap.
      kind (str or None): Whether to use test or train tasks.
      wrapper (Callable[garage.Env, garage.Env] or None): Wrapper to apply to
          env instances.
      add_env_onehot (bool): If true, a one-hot representing the current
          environment name will be added to the environments. Should only be
          used with multi-task benchmarks.
  
  Raises:
      ValueError: If kind is not 'train', 'test', or None. Also raisd if
          `add_env_onehot` is used on a metaworld meta learning (not
          multi-task) benchmark.
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docstr: akro.Space: The action space specification.
docstr_tok: ["akro.Space",":","The","action","space","specification","."]
code: def action_space(self):
      """akro.Space: The action space specification."""
      return self._current_env.action_space
  
imports: import random
  from garage import envs
  from garage._environment import Environment
label_desc: Use image node_id 8 for calling the MetaWorldSetTaskEnv obj's underlying member method code with example usage: obj.action_space() and returns: self