class_docstr: Simple Vanilla Policy Gradient.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      policy (garage.tf.policies.StochasticPolicy): Policy.
      sampler (garage.sampler.Sampler): Sampler.
class_docstr_tok: ["Simple","Vanilla","Policy","Gradient",".","Args",":","env_spec","(","EnvSpec",")",":","Environment","specification",".","policy","(","garage.tf.policies.StochasticPolicy",")",":","Policy",".","sampler","(","garage.sampler.Sampler",")",":","Sampler","."]
docstr: Parameters to save in snapshot.
  
  Returns:
      dict: Parameters to save.
docstr_tok: ["Parameters","to","save","in","snapshot",".","Returns",":","dict",":","Parameters","to","save","."]
code: def __getstate__(self):
      """Parameters to save in snapshot.
  
      Returns:
          dict: Parameters to save.
  
      """
      data = self.__dict__.copy()
      del data["_observation"]
      del data["_action"]
      del data["_returns"]
      del data["_train_op"]
      return data
  
file_path: garage/src/garage/examples/tf/tutorial_vpg.py
imports: import numpy
  import tensorflow
  from garage import EpisodeBatch, log_performance, wrap_experiment
  from garage.envs import PointEnv
  from garage.experiment.deterministic import set_seed
  from garage.np import discount_cumsum
  from garage.sampler import LocalSampler
  from garage.tf.policies import GaussianMLPPolicy
  from garage.trainer import TFTrainer
label_desc: Use image node_id 5 for calling the SimpleVPG obj's underlying member method code with example usage: obj.__getstate__() and returns: data