class_docstr: This class evaluates policy of OpenAI Gym environment.
  
  Parameters
  -----------
  env_name: str
      Gym environment name
  state_mean: list
      Average state values of multiple independent runs.
  state_std: list
      Standard deviation of state values of multiple independent runs.
  num_rollouts: int
      number of independent runs.
  activation: str:
      activation function
  layer_rescaling_coef: float
      Scaling coefficient of output layers
  random_state: int or None
      random state for reproducibility in Gym environment.
class_docstr_tok: ["This","class","evaluates","policy","of","OpenAI","Gym","environment",".","Parameters","--","--","--","--","--","-","env_name",":","str","Gym","environment","name","state_mean",":","list","Average","state","values","of","multiple","independent","runs",".","state_std",":","list","Standard","deviation","of","state","values","of","multiple","independent","runs",".","num_rollouts",":","int","number","of","independent","runs",".","activation",":","str",":","activation","function","layer_rescaling_coef",":","float","Scaling","coefficient","of","output","layers","random_state",":","int","or","None","random","state","for","reproducibility","in","Gym","environment","."]
code: def _activation(self, x):
      if self.activation == "tanh":
          return np.tanh(x)
      elif self.activation == "sigmoid":
          return 1.0 / (1 + np.exp(-x))
      else:
          raise NotImplementedError(
              r"Activation {self.activation} not implemented."
          )
  
imports: import gym
  import numpy
label_desc: Use image node_id 2 for calling the GenericMujocoEnv obj's underlying member method code with example usage: obj._activation(x) and returns: np, unknown