code: def test_without_std_share_network_output_values(
      self, mock_normal, output_dim, hidden_dim, init_std
  ):
      mock_normal.return_value = 0.5
      model = GaussianGRUModel(
          output_dim=output_dim,
          hidden_dim=hidden_dim,
          std_share_network=False,
          hidden_nonlinearity=None,
          recurrent_nonlinearity=None,
          hidden_w_init=self.default_initializer,
          recurrent_w_init=self.default_initializer,
          output_w_init=self.default_initializer,
          init_std=init_std,
      )
      step_hidden_var = tf.compat.v1.placeholder(
          shape=(self.batch_size, hidden_dim),
          name="step_hidden",
          dtype=tf.float32,
      )
      (
          _,
          step_mean_var,
          step_log_std_var,
          step_hidden,
          hidden_init_var,
      ) = model.build(
          self.input_var, self.step_input_var, step_hidden_var
      ).outputs
  
      hidden1 = hidden2 = np.full(
          (self.batch_size, hidden_dim), hidden_init_var.eval()
      )
  
      for _ in range(self.time_step):
          mean1, log_std1, hidden1 = self.sess.run(
              [step_mean_var, step_log_std_var, step_hidden],
              feed_dict={
                  self.step_input_var: self.obs_input,
                  step_hidden_var: hidden1,
              },
          )
  
          hidden2 = recurrent_step_gru(
              input_val=self.obs_input,
              num_units=hidden_dim,
              step_hidden=hidden2,
              w_x_init=0.1,
              w_h_init=0.1,
              b_init=0.0,
              nonlinearity=None,
              gate_nonlinearity=None,
          )
  
          output_nonlinearity = np.full(
              (np.prod(hidden2.shape[1:]), output_dim), 0.1
          )
          output2 = np.matmul(hidden2, output_nonlinearity)
          assert np.allclose(mean1, output2)
          expected_log_std = np.full(
              (self.batch_size, output_dim), np.log(init_std)
          )
          assert np.allclose(log_std1, expected_log_std)
          assert np.allclose(hidden1, hidden2)
  
file_path: garage/tests/garage/tf/models/test_gaussian_gru_model.py
imports: import pickle
  from unittest import mock
  import numpy
  import pytest
  import tensorflow
  import tensorflow_probability
  from garage.tf.models import GaussianGRUModel
  from tests.fixtures import TfGraphTestCase
  from tests.helpers import recurrent_step_gru
label_desc: Use image node_id 5 for calling the TestGaussianGRUModel obj's underlying member method code with example usage: obj.test_without_std_share_network_output_values(mock_normal, output_dim, hidden_dim, init_std) without return types