code: def test_is_pickleable(self, output_dim, hidden_sizes):
      model = MLPModel(
          output_dim=output_dim,
          hidden_sizes=hidden_sizes,
          hidden_nonlinearity=None,
          hidden_w_init=tf.ones_initializer(),
          output_w_init=tf.ones_initializer(),
      )
      outputs = model.build(self.input_var).outputs
  
      # assign bias to all one
      with tf.compat.v1.variable_scope("MLPModel/mlp", reuse=True):
          bias = tf.compat.v1.get_variable("hidden_0/bias")
  
      bias.load(tf.ones_like(bias).eval())
  
      output1 = self.sess.run(
          outputs, 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, 5)
          )
          model_pickled = pickle.loads(h)
          outputs = model_pickled.build(input_var).outputs
          output2 = sess.run(outputs, feed_dict={input_var: self.obs})
  
          assert np.array_equal(output1, output2)
  
file_path: garage/tests/garage/tf/models/test_mlp_model.py
imports: import pickle
  import numpy
  import pytest
  import tensorflow
  from garage.tf.models import MLPDuelingModel, MLPMergeModel, MLPModel
  from tests.fixtures import TfGraphTestCase
label_desc: Use image node_id 5 for calling the TestMLPModel obj's underlying member method code with example usage: obj.test_is_pickleable(output_dim, hidden_sizes) without return types