code: def test_compute_advantage(self):
      if not self.has_maximizer:
          return
      sample_actions = np.random.randn(
          self.batch_size, self.action_size
      ).astype(np.float32)
      sample_actions = torch.from_numpy(sample_actions)
      ret = self.av.compute_advantage(sample_actions)
      assertions.assertIsInstance(ret, torch.Tensor)
      np.testing.assert_equal(
          ret.numpy(),
          (
              self.evaluator(sample_actions).numpy()
              - self.evaluator(self.maximizer()).numpy()
          ),
      )
  
imports: import unittest
  import numpy
  import pytest
  import torch
  from pfrl import action_value
  from pfrl.testing import torch_assert_allclose
label_desc: Use image node_id 5 for calling the TestSingleActionValue obj's underlying member method code with example usage: obj.test_compute_advantage() without return types