code: def test_uniformly_weighted(self):
      # Uniform weights of size batch size
      w1 = np.ones(self.y.shape[0], dtype="f")
  
      loss_w1 = compute_weighted_value_loss(
          self.eltwise_losses,
          self.y.shape[0],
          w1,
          batch_accumulator=self.batch_accumulator,
      ).array
      if self.batch_accumulator == "mean":
          eltwise_loss = self.eltwise_losses.sum(axis=1).mean()
      else:
          eltwise_loss = self.eltwise_losses.sum()
      self.assertAlmostEqual(loss_w1, 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 3 for calling the TestComputeValueLoss obj's underlying member method code with example usage: obj.test_uniformly_weighted() without return types