code: def test_fit_normalized(self):
      box_env_spec = GymEnv(DummyBoxEnv(obs_dim=(2,))).spec
      gmb = GaussianMLPBaseline(env_spec=box_env_spec)
  
      (
          train_paths,
          observations,
          returns,
          paths,
          expected,
      ) = get_train_test_data()
  
      for _ in range(150):
          gmb.fit(train_paths)
  
      prediction = gmb.predict(paths)
  
      assert np.allclose(prediction, expected, rtol=0, atol=0.1)
  
      x_mean = self.sess.run(gmb._networks["default"].x_mean)
      x_mean_expected = np.mean(observations, axis=0, keepdims=True)
      x_std = self.sess.run(gmb._networks["default"].x_std)
      x_std_expected = np.std(observations, axis=0, keepdims=True)
  
      assert np.allclose(x_mean, x_mean_expected)
      assert np.allclose(x_std, x_std_expected)
  
      y_mean = self.sess.run(gmb._networks["default"].y_mean)
      y_mean_expected = np.mean(returns, axis=0, keepdims=True)
      y_std = self.sess.run(gmb._networks["default"].y_std)
      y_std_expected = np.std(returns, axis=0, keepdims=True)
  
      assert np.allclose(y_mean, y_mean_expected)
      assert np.allclose(y_std, y_std_expected)
  
file_path: garage/tests/garage/tf/baselines/test_gaussian_mlp_baseline.py
imports: import pickle
  from unittest import mock
  import numpy
  import pytest
  import tensorflow
  from garage.envs import GymEnv
  from garage.tf.baselines import GaussianMLPBaseline
  from tests.fixtures import TfGraphTestCase
  from tests.fixtures.envs.dummy import DummyBoxEnv
label_desc: Use image node_id 1 for calling the TestGaussianMLPBaseline obj's underlying member method code with example usage: obj.test_fit_normalized() without return types