code: def test_randomly_weighted(self):
      # Random weights
      wu = np.random.uniform(
          low=0, high=2, size=self.y.shape[0]
      ).astype("f")
  
      loss_wu = compute_weighted_value_loss(
          self.eltwise_losses,
          self.y.shape[0],
          wu,
          batch_accumulator=self.batch_accumulator,
      ).array
      if self.batch_accumulator == "mean":
          eltwise_loss = (self.eltwise_losses.sum(axis=1) * wu).mean()
      else:
          eltwise_loss = (self.eltwise_losses * wu[:, None]).sum()
      self.assertAlmostEqual(loss_wu, eltwise_loss, places=5)
  
imports: import unittest
  import chainer
  import chainer.links
  from chainer import testing
  import numpy
  import basetest_dqn_like
  from basetest_training import _TestBatchTrainingMixin
  import chainerrl
  from chainerrl.agents import categorical_dqn
  from chainerrl.agents.categorical_dqn import compute_value_loss
  from chainerrl.agents.categorical_dqn import compute_weighted_value_loss
  from chainerrl.agents import CategoricalDQN
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