code: def test_uniformly_weighted(self):
      # Uniform weights
      w1 = torch.ones(self.y.size())
  
      loss_w1 = compute_weighted_value_loss(
          self.y,
          self.t,
          clip_delta=self.clip_delta,
          batch_accumulator=self.batch_accumulator,
          weights=w1,
      )
      if self.batch_accumulator == "mean":
          gt_loss = self.gt_losses.mean()
      else:
          gt_loss = self.gt_losses.sum()
      assertions.assertAlmostEqual(
          loss_w1.numpy(), gt_loss.numpy(), places=5
      )
  
imports: import unittest
  import basetest_dqn_like
  import pytest
  import torch
  from basetest_training import _TestActorLearnerTrainingMixin, _TestBatchTrainingMixin
  import pfrl
  from pfrl.agents.dqn import DQN, compute_value_loss, compute_weighted_value_loss
label_desc: Use image node_id 3 for calling the TestComputeValueLoss obj's underlying member method code with example usage: obj.test_uniformly_weighted() without return types