code: def test_without_std_share_network_is_pickleable(
      self, mock_normal, output_dim, hidden_dim
  ):
      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,
      )
      step_hidden_var = tf.compat.v1.placeholder(
          shape=(self.batch_size, hidden_dim),
          name="step_hidden",
          dtype=tf.float32,
      )
      (
          dist,
          step_mean_var,
          step_log_std_var,
          step_hidden,
          _,
      ) = model.build(
          self.input_var, self.step_input_var, step_hidden_var
      ).outputs
  
      # output layer is a tf.keras.layers.Dense object,
      # which cannot be access by tf.compat.v1.variable_scope.
      # A workaround is to access in tf.compat.v1.global_variables()
      for var in tf.compat.v1.global_variables():
          if "output_layer/bias" in var.name:
              var.load(tf.ones_like(var).eval())
  
      hidden = np.zeros((self.batch_size, hidden_dim))
      outputs1 = self.sess.run(
          [dist.loc, dist.scale.diag],
          feed_dict={self.input_var: self.obs_inputs},
      )
      output1 = self.sess.run(
          [step_mean_var, step_log_std_var, step_hidden],
          feed_dict={
              self.step_input_var: self.obs_input,
              step_hidden_var: hidden,
          },
      )  # noqa: E126
  
      h = pickle.dumps(model)
      with tf.compat.v1.Session(graph=tf.Graph()) as sess:
          model_pickled = pickle.loads(h)
  
          input_var = tf.compat.v1.placeholder(
              tf.float32,
              shape=(None, None, self.feature_shape),
              name="input",
          )
          step_input_var = tf.compat.v1.placeholder(
              tf.float32,
              shape=(None, self.feature_shape),
              name="step_input",
          )
          step_hidden_var = tf.compat.v1.placeholder(
              shape=(self.batch_size, hidden_dim),
              name="initial_hidden",
              dtype=tf.float32,
          )
  
          (
              dist2,
              step_mean_var2,
              step_log_std_var2,
              step_hidden2,
              _,
          ) = model_pickled.build(
              input_var, step_input_var, step_hidden_var
          ).outputs
  
          outputs2 = sess.run(
              [dist2.loc, dist2.scale.diag],
              feed_dict={input_var: self.obs_inputs},
          )
          output2 = sess.run(
              [step_mean_var2, step_log_std_var2, step_hidden2],
              feed_dict={
                  step_input_var: self.obs_input,
                  step_hidden_var: hidden,
              },
          )
          assert np.array_equal(outputs1, outputs2)
          assert np.array_equal(output1, output2)
  
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 8 for calling the TestGaussianGRUModel obj's underlying member method code with example usage: obj.test_without_std_share_network_is_pickleable(mock_normal, output_dim, hidden_dim) without return types