class_docstr: Noop wrapper for gym.Env.
  
  It samples initial states by taking random number of no-ops on reset.
  No-op is assumed to be action 0.
  
  Args:
      env (gym.Env): The environment to be wrapped.
      noop_max (int): Maximum number no-op to be performed on reset.
class_docstr_tok: ["Noop","wrapper","for","gym.Env",".","It","samples","initial","states","by","taking","random","number","of","no-ops","on","reset",".","No-op","is","assumed","to","be","action","0",".","Args",":","env","(","gym.Env",")",":","The","environment","to","be","wrapped",".","noop_max","(","int",")",":","Maximum","number","no-op","to","be","performed","on","reset","."]
docstr: gym.Env step function.
docstr_tok: ["gym.Env","step","function","."]
code: def step(self, action):
      """gym.Env step function."""
      return self.env.step(action)
  
imports: import gym
  import numpy
label_desc: Use image node_id 2 for calling the Noop obj's underlying member method code with example usage: obj.step(action) and returns: self