code: def get_params_of_distribution(distrib):
      if isinstance(distrib, torch.distributions.Independent):
          return get_params_of_distribution(distrib.base_dist)
      elif isinstance(distrib, torch.distributions.Categorical):
          return (distrib._param,)
      elif isinstance(distrib, torch.distributions.Normal):
          return distrib.loc, distrib.scale
      else:
          raise NotImplementedError(
              "{} is not supported by ACER".format(type(distrib))
          )
  
imports: import copy
  from logging import getLogger
  import numpy
  import torch
  from torch import nn
  from pfrl import agent
  from pfrl.action_value import SingleActionValue
  from pfrl.utils import clip_l2_grad_norm_, copy_param
  from pfrl.utils.batch_states import batch_states
  from pfrl.utils.mode_of_distribution import mode_of_distribution
  from pfrl.utils.recurrent import detach_recurrent_state, one_step_forward
label_desc: Use image node_id 6 for calling a global function with example usage: get_params_of_distribution(distrib) and returns: get_params_of_distribution, distrib, distrib,  distrib