code: def _set_up_linear_regression(self, sample_weight=1.0):
      w = np.array([[4.0], [-3.0]])
      x0 = np.array([[2.0, 3.0]])
      y0 = np.array([[0.0]])
      adv_multiplier = 0.2
      adv_step_size = 0.01
      learning_rate = 0.01
      adv_config = configs.make_adv_reg_config(
          multiplier=adv_multiplier,
          adv_step_size=adv_step_size,
          adv_grad_norm="infinity",
      )
      y_hat = np.dot(x0, w)
      x_adv = x0 + adv_step_size * np.sign((y_hat - y0) * w.T)
      y_hat_adv = np.dot(x_adv, w)
      grad_w_labeled_loss = sample_weight * 2.0 * (y_hat - y0) * x0.T
      grad_w_adv_loss = (
          adv_multiplier
          * sample_weight
          * 2.0
          * (y_hat_adv - y0)
          * x_adv.T
      )
      w_new = w - learning_rate * (
          grad_w_labeled_loss + grad_w_adv_loss
      )
      return w, x0, y0, learning_rate, adv_config, w_new
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  from absl.testing import parameterized
  import neural_structured_learning.configs
  from neural_structured_learning.keras import adversarial_regularization
  import numpy
  import tensorflow
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 3 for calling the AdversarialRegularizationTest obj's underlying member method code with example usage: obj._set_up_linear_regression(sample_weight) and returns: w,  x0,  y0,  learning_rate,  adv_config,  w_new