class_docstr: Simple Cross Entropy Method.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      policy (Policy): Action policy.
      sampler (garage.sampler.Sampler): Sampler.
docstr: Return sample parameters.
  
  Args:
      epoch (int): Epoch number.
  
  Returns:
      np.ndarray: A numpy array of parameter values.
docstr_tok: ["Return","sample","parameters",".","Args",":","epoch","(","int",")",":","Epoch","number",".","Returns",":","np.ndarray",":","A","numpy","array","of","parameter","values","."]
code: def _sample_params(self, epoch):
      """Return sample parameters.
  
      Args:
          epoch (int): Epoch number.
  
      Returns:
          np.ndarray: A numpy array of parameter values.
  
      """
      extra_var_mult = max(1.0 - epoch / self._extra_decay_time, 0)
      sample_std = np.sqrt(
          np.square(self._cur_std)
          + np.square(self._extra_std) * extra_var_mult
      )
  
      return (
          np.random.standard_normal(len(self._cur_mean)) * sample_std
          + self._cur_mean
      )
  
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 4 for calling the SimpleCEM obj's underlying member method code with example usage: obj._sample_params(epoch) and returns: unknown