code: def step(self, obs):
      with torch.no_grad():
          pi = self.pi._distribution(obs)
          a = pi.sample()
          logp_a = self.pi._log_prob_from_distribution(pi, a)
          v = self.v(obs)
      return a.numpy(), v.numpy(), logp_a.numpy()
  
imports: import numpy
  import scipy.signal
  from gym.spaces import Box, Discrete
  import torch
  import torch.nn
  from torch.distributions.normal import Normal
  from torch.distributions.categorical import Categorical
label_desc: Use image node_id 2 for calling the MLPActorCritic obj's underlying member method code with example usage: obj.step(obs) and returns: a,  v,  logp_a