code: def test_forward_pass(self):
      # Function to test forward pass of nn_params.
      net_weights = [[[2, 2], [3, 3], [4, 4]], [1, 1, 1]]
      net_biases = [
          np.transpose(np.matrix([0, 0, 0])),
          np.transpose(np.matrix([0, 0])),
      ]
      net_layer_types = ["ff_relu", "ff"]
      nn_params = nn.NeuralNetwork(
          net_weights, net_biases, net_layer_types
      )
  
      input_vector = tf.random_uniform(shape=(2, 1), dtype=tf.float32)
      output_vector = nn_params.forward_pass(input_vector, 0)
      self.assertEqual(output_vector.shape.as_list(), [3, 1])
      output_vector_2 = nn_params.forward_pass(
          input_vector, 0, is_abs=True
      )
      self.assertEqual(output_vector_2.shape.as_list(), [3, 1])
      input_vector_trans = tf.random_uniform(
          shape=(3, 1), dtype=tf.float32
      )
      output_vector_3 = nn_params.forward_pass(
          input_vector_trans, 0, is_transpose=True
      )
      self.assertEqual(output_vector_3.shape.as_list(), [2, 1])
  
file_path: cleverhans/cleverhans_v3.1.0/cleverhans/experimental/certification/tests/nn_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import unittest
  import numpy
  import tensorflow
  from cleverhans.experimental.certification import nn
label_desc: Use image node_id 2 for calling the NeuralNetworkTest obj's underlying member method code with example usage: obj.test_forward_pass() without return types