code: def setUp(self, batch_accumulator):
      self.batch_accumulator = batch_accumulator
      # y and t are (batchsize, n_atoms)
      self.y = np.asarray(
          [[0.1, 0.2, 0.3, 0.4], [0.05, 0.1, 0.2, 0.65]], dtype="f"
      )
      self.t = np.asarray(
          [[0.2, 0.2, 0.2, 0.4], [0.1, 0.3, 0.3, 0.3]], dtype="f"
      )
      self.eltwise_losses = np.asarray(
          [categorical_loss(a, b) for a, b in zip(self.y, self.t)]
      )
  
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 1 for calling the TestComputeValueLoss obj's underlying member method code with example usage: obj.setUp(batch_accumulator) without return types