code: def test_policy_gradient(self):
      action = self.mu.sample()
      pg = acer.compute_policy_gradient_loss(
          action,
          1,
          self.pi,
          self.mu,
          self.action_value,
          0,
          self.truncation_threshold,
      )
      assert isinstance(pg, torch.Tensor)
      print("pg", pg)
      assert not np.isnan(pg.numpy()).any()
  
imports: import copy
  import logging
  import os
  import tempfile
  import warnings
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  from pfrl.agents import acer
  from pfrl.envs.abc import ABC
  from pfrl.experiments.evaluator import run_evaluation_episodes
  from pfrl.experiments.train_agent_async import train_agent_async
  from pfrl.nn import ConcatObsAndAction
  from pfrl.policies import GaussianHeadWithDiagonalCovariance, SoftmaxCategoricalHead
  from pfrl.q_functions import DiscreteActionValueHead
  from pfrl.replay_buffers import EpisodicReplayBuffer
label_desc: Use image node_id 6 for calling the TestDegenerateDistribution obj's underlying member method code with example usage: obj.test_policy_gradient() without return types