class_docstr: Simple Cross Entropy Method.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      policy (Policy): Action policy.
      sampler (garage.sampler.Sampler): Sampler.
code: def __init__(self, env_spec, policy, sampler):
      self.env_spec = env_spec
      self.policy = policy
      self._sampler = sampler
      self.max_episode_length = env_spec.max_episode_length
      self._discount = 0.99
      self._extra_std = 1
      self._extra_decay_time = 100
      self._n_samples = 20
      self._n_best = 1
      self._cur_std = 1
      self._cur_mean = self.policy.get_param_values()
      self._all_avg_returns = []
      self._all_params = [self._cur_mean.copy()]
      self._cur_params = None
  
file_path: garage/src/garage/examples/np/tutorial_cem.py
imports: import numpy
  from garage import EpisodeBatch, log_performance, wrap_experiment
  from garage.envs import GymEnv
  from garage.experiment.deterministic import set_seed
  from garage.np import discount_cumsum
  from garage.sampler import LocalSampler
  from garage.tf.policies import CategoricalMLPPolicy
  from garage.trainer import TFTrainer
label_desc: Use image node_id 1 to create a new SimpleCEM object  with example: obj = SimpleCEM(env_spec, policy, sampler)