code: def deepcopy_distribution(distrib):
      """Deepcopy a PyTorch distribution.
  
      PyTorch distributions cannot be deepcopied as it is except its tensors are
      graph leaves.
      """
      if isinstance(distrib, torch.distributions.Independent):
          return torch.distributions.Independent(
              deepcopy_distribution(distrib.base_dist),
              distrib.reinterpreted_batch_ndims,
          )
      elif isinstance(distrib, torch.distributions.Categorical):
          return torch.distributions.Categorical(
              logits=distrib.logits.clone().detach(),
          )
      elif isinstance(distrib, torch.distributions.Normal):
          return torch.distributions.Normal(
              loc=distrib.loc.clone().detach(),
              scale=distrib.scale.clone().detach(),
          )
      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 7 for calling a global function with example usage: deepcopy_distribution(distrib) and returns: torch, torch, torch