code: def fix_get_synthetic_data():
      """
      As real malware is hard to share, generate random data of the correct size
      """
      # generate dummy data
      padding_char = 256
      maxlen = 2**20
  
      # batch of 5 datapoints
      synthetic_data = (
          np.ones((5, maxlen), dtype=np.uint16) * padding_char
      )
  
      size_of_original_files = [
          int(
              maxlen * 0.1
          ),  # 1 sample significantly smaller than the maxlen
          int(maxlen * 1.5),  # 1 sample larger then maxlen
          int(
              maxlen * 0.95
          ),  # 1 sample close to the maximum of the maxlen
          int(maxlen),  # 1 sample at the maxlen
          int(maxlen),
      ]  # 1 sample at the maxlen, this will be assigned a benign label and
      # should not be perturbed by the attack.
  
      # two class option, later change to binary when ART is generally updated.
      y = np.zeros((5, 1))
      y[
          0:4
      ] = 1  # assign the first 4 datapoints to be labeled as malware
  
      # fill in with random numbers
      for i, size in enumerate(size_of_original_files):
          if size > maxlen:
              size = maxlen
          synthetic_data[i, 0:size] = np.random.randint(
              low=0, high=256, size=(1, size)
          )
  
      # set the DOS header values:
      synthetic_data[:, 0:2] = [77, 90]
      synthetic_data[
          :, int(0x3C) : int(0x40)
      ] = 0  # zero the pointer location
      synthetic_data[:, int(0x3C)] = 44  # put in a dummy pointer
      synthetic_data[:, int(0x3C) + 1] = 1  # put in a dummy pointer
      return synthetic_data, y, np.asarray(size_of_original_files)
  
file_path: adversarial-robustness-toolbox/tests/attacks/evasion/test_pe_malware_attack.py
imports: import pytest
  import tensorflow
  import numpy
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from art.attacks.evasion.pe_malware_attack import MalwareGDTensorFlow
  from art.estimators.classification import TensorFlowV2Classifier
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from tests.utils import ARTTestException
label_desc: Use image node_id 1 for calling a global function with example usage: fix_get_synthetic_data() and returns: synthetic_data,  y,  np