class_docstr: Base class of trainer.
  
  Use trainer.setup(algo, env) to setup algorithm and environment for trainer
  and trainer.train() to start training.
  
  Args:
      snapshot_config (garage.experiment.SnapshotConfig): The snapshot
          configuration used by Trainer to create the snapshotter.
          If None, it will create one with default settings.
  
  Note:
      For the use of any TensorFlow environments, policies and algorithms,
      please use TFTrainer().
  
  Examples:
      | # to train
      | trainer = Trainer()
      | env = Env(...)
      | policy = Policy(...)
      | algo = Algo(
      |         env=env,
      |         policy=policy,
      |         ...)
      | trainer.setup(algo, env)
      | trainer.train(n_epochs=100, batch_size=4000)
  
      | # to resume immediately.
      | trainer = Trainer()
      | trainer.restore(resume_from_dir)
      | trainer.resume()
  
      | # to resume with modified training arguments.
      | trainer = Trainer()
      | trainer.restore(resume_from_dir)
      | trainer.resume(n_epochs=20)
docstr: Get a copy of the environment.
  
  Returns:
      Environment: An environment instance.
docstr_tok: ["Get","a","copy","of","the","environment",".","Returns",":","Environment",":","An","environment","instance","."]
code: def get_env_copy(self):
      """Get a copy of the environment.
  
      Returns:
          Environment: An environment instance.
  
      """
      if self._env:
          return cloudpickle.loads(cloudpickle.dumps(self._env))
      else:
          return None
  
imports: import copy
  import os
  import time
  import cloudpickle
  from dowel import logger, tabular
  from garage.experiment.deterministic import get_seed, set_seed
  from garage.experiment.experiment import dump_json
  from garage.experiment.snapshotter import Snapshotter
label_desc: Use image node_id 13 for calling the Trainer obj's underlying member method code with example usage: obj.get_env_copy() and returns: cloudpickle, None