code: def test_jacobian_for_levenberg_marquardt(self):
      w1 = tf.Variable(asfloat(np.array([[1]])), name="w1")
      b1 = tf.Variable(asfloat(np.array([0])), name="b1")
      w2 = tf.Variable(asfloat(np.array([[2]])), name="w2")
      b2 = tf.Variable(asfloat(np.array([1])), name="b2")
  
      x = asfloat(np.array([[1, 2, 3]]).T)
      y = asfloat(np.array([[1, 2, 3]]).T)
  
      output_1 = tf.matmul(x, tf.transpose(w1)) + b1
      output = b2 + tf.matmul(output_1**2, w2)
      error_func = tf.reduce_mean((y - output), axis=1)
  
      output_expected = asfloat(np.array([[3, 9, 19]]).T)
  
      np.testing.assert_array_almost_equal(
          self.eval(output), output_expected
      )
  
      jacobian_expected = asfloat(
          np.array(
              [
                  [-4, -4, -1, -1],
                  [-16, -8, -4, -1],
                  [-36, -12, -9, -1],
              ]
          )
      )
      jacobian_actual = compute_jacobian(error_func, [w1, b1, w2, b2])
      np.testing.assert_array_almost_equal(
          jacobian_expected, self.eval(jacobian_actual)
      )
  
file_path: neupy/tests/algorithms/gd/test_levmarq.py
imports: from functools import partial
  import numpy
  import tensorflow
  from sklearn import datasets, preprocessing
  from sklearn.model_selection import train_test_split
  from neupy import algorithms, layers
  from neupy.utils import asfloat
  from neupy.algorithms.gd.lev_marq import compute_jacobian
  from base import BaseTestCase
label_desc: Use image node_id 1 for calling the LevenbergMarquardtTestCase obj's underlying member method code with example usage: obj.test_jacobian_for_levenberg_marquardt() without return types