code: def test_not_weighted(self):
      loss = (
          compute_value_loss(
              torch.as_tensor(self.eltwise_losses),
              batch_accumulator=self.batch_accumulator,
          )
          .detach()
          .cpu()
          .numpy()
      )
      if self.batch_accumulator == "mean":
          eltwise_loss = self.eltwise_losses.sum(axis=1).mean()
      else:
          eltwise_loss = self.eltwise_losses.sum()
      assertions.assertAlmostEqual(loss, eltwise_loss, places=5)
  
imports: import unittest
  import basetest_dqn_like
  import numpy
  import pytest
  import torch
  from basetest_training import _TestBatchTrainingMixin
  import pfrl
  from pfrl.agents import CategoricalDQN, categorical_dqn
  from pfrl.agents.categorical_dqn import compute_value_loss, compute_weighted_value_loss
label_desc: Use image node_id 2 for calling the TestComputeValueLoss obj's underlying member method code with example usage: obj.test_not_weighted() without return types