code: def test_large(self):
      en = empirical_normalization.EmpiricalNormalization(10)
      for _ in range(10000):
          x = np.random.normal(loc=4, scale=2, size=(7, 10))
          en(torch.tensor(x))
      x = 2 * np.random.normal(loc=4, scale=2, size=(1, 10))
      enx = en(torch.tensor(x), update=False)
  
      np.testing.assert_allclose(en.mean.cpu().numpy(), 4, rtol=1e-1)
      np.testing.assert_allclose(en.std.cpu().numpy(), 2, rtol=1e-1)
  
      # Compare with the ground-truth normalization
      np.testing.assert_allclose((x - 4) / 2, enx, rtol=1e-1)
  
      # Test inverse
      np.testing.assert_allclose(
          x, en.inverse(torch.tensor(enx)), rtol=1e-4
      )
  
file_path: pfrl/tests/nn_tests/test_empirical_normalization.py
imports: import unittest
  import numpy
  import pytest
  import torch
  from pfrl.nn import empirical_normalization
label_desc: Use image node_id 4 for calling the TestEmpiricalNormalization obj's underlying member method code with example usage: obj.test_large() without return types