code: def test_randomly_weighted(self):
      # Random weights
      wu = torch.empty(self.y.size())
      torch.nn.init.uniform_(wu, a=0, b=2)
  
      loss_wu = compute_weighted_value_loss(
          self.y,
          self.t,
          clip_delta=self.clip_delta,
          batch_accumulator=self.batch_accumulator,
          weights=wu,
      )
      if self.batch_accumulator == "mean":
          gt_loss = (self.gt_losses * wu).mean()
      else:
          gt_loss = (self.gt_losses * wu).sum()
      assertions.assertAlmostEqual(
          loss_wu.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 4 for calling the TestComputeValueLoss obj's underlying member method code with example usage: obj.test_randomly_weighted() without return types