code: def _test(self, device):
      v_min, v_max = self.v_range
      z = np.linspace(v_min, v_max, num=self.n_atoms, dtype=np.float32)
      y = np.random.normal(size=(self.batch_size, self.n_atoms)).astype(
          np.float32
      )
      y_probs = np.asarray(
          np.random.dirichlet(
              alpha=np.ones(self.n_atoms), size=self.batch_size
          ).astype(np.float32)
      )
  
      # Naive implementation as ground truths
      proj_gt = _apply_categorical_projection_naive(y, y_probs, z)
      # Projected probabilities should sum to one
      np.testing.assert_allclose(
          proj_gt.sum(axis=1),
          np.ones(self.batch_size, dtype=np.float32),
          atol=1e-5,
      )
  
      # Batch implementation to test
      proj = (
          categorical_dqn._apply_categorical_projection(
              torch.as_tensor(y, device=device),
              torch.as_tensor(y_probs, device=device),
              torch.as_tensor(z, device=device),
          )
          .detach()
          .cpu()
          .numpy()
      )
      # Projected probabilities should sum to one
      np.testing.assert_allclose(
          proj.sum(axis=1),
          np.ones(self.batch_size, dtype=np.float32),
          atol=1e-5,
      )
  
      # Both should be equal
      np.testing.assert_allclose(proj, proj_gt, atol=1e-5)
  
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 2 for calling the TestApplyCategoricalProjectionToRandomCases obj's underlying member method code with example usage: obj._test(device) without return types