code: def test_without_std_share_network_is_pickleable(
      self, output_dim, hidden_sizes
  ):
      model = GaussianMLPModel(
          output_dim=output_dim,
          hidden_sizes=hidden_sizes,
          std_share_network=False,
          adaptive_std=False,
          hidden_nonlinearity=None,
          hidden_w_init=tf.ones_initializer(),
          output_w_init=tf.ones_initializer(),
      )
      dist = model.build(self.input_var).dist
  
      # get output bias
      with tf.compat.v1.variable_scope("GaussianMLPModel", reuse=True):
          bias = tf.compat.v1.get_variable(
              "dist_params/mean_network/output/bias"
          )
      # assign it to all ones
      bias.load(tf.ones_like(bias).eval())
  
      output1 = self.sess.run(
          [dist.loc, tf.math.log(dist.stddev())],
          feed_dict={self.input_var: self.obs},
      )
  
      h = pickle.dumps(model)
      with tf.compat.v1.Session(graph=tf.Graph()) as sess:
          input_var = tf.compat.v1.placeholder(
              tf.float32, shape=(None, None, 5)
          )
          model_pickled = pickle.loads(h)
          dist2 = model_pickled.build(input_var).dist
          output2 = sess.run(
              [dist2.loc, tf.math.log(dist2.stddev())],
              feed_dict={input_var: self.obs},
          )
          assert np.array_equal(output1, output2)
  
imports: import pickle
  import numpy
  import pytest
  import tensorflow
  import tensorflow_probability
  from garage.tf.models import GaussianMLPModel
  from tests.fixtures import TfGraphTestCase
label_desc: Use image node_id 10 for calling the TestGaussianMLPModel obj's underlying member method code with example usage: obj.test_without_std_share_network_is_pickleable(output_dim, hidden_sizes) without return types