code: def test_different_mlp(self):
      # We create another mlp with different name
      with tf.compat.v1.variable_scope("MLP_Concat"):
          self.mlp_different_copy = mlp(
              input_var=self._obs_input,
              output_dim=self._output_shape,
              hidden_sizes=(32, 32),
              input_var2=self._act_input,
              concat_layer=0,
              hidden_nonlinearity=self.hidden_nonlinearity,
              name="mlp2",
          )
  
      # Initialize the new mlp variables
      self.sess.run(tf.compat.v1.global_variables_initializer())
  
      # We modify the weight of the default mlp and feed
      # The another mlp created should output different result
      with tf.compat.v1.variable_scope("MLP_Concat", reuse=True):
          w = tf.compat.v1.get_variable("mlp1/hidden_0/kernel")
          self.sess.run(w.assign(w + 1))
          mlp_output = self.sess.run(
              self.mlp_f,
              feed_dict={
                  self._obs_input: self.obs_input,
                  self._act_input: self.act_input,
              },
          )
          mlp_output2 = self.sess.run(
              self.mlp_different_copy,
              feed_dict={
                  self._obs_input: self.obs_input,
                  self._act_input: self.act_input,
              },
          )
  
      assert not np.array_equal(mlp_output, mlp_output2)
  
file_path: garage/tests/garage/tf/models/test_mlp_concat.py
imports: import numpy
  import pytest
  import tensorflow
  from garage.tf.models.mlp import mlp
  from tests.fixtures import TfGraphTestCase
label_desc: Use image node_id 3 for calling the TestMLPConcat obj's underlying member method code with example usage: obj.test_different_mlp() without return types