class_docstr: Implementation of the following white-box attacks related to PE malware crafting:
      1) Append based attacks (example paper link: https://arxiv.org/abs/1810.08280)
      2) Section insertion attacks (example paper link: https://arxiv.org/abs/2008.07125)
      3) Slack manipulation attacks (example paper link: https://arxiv.org/abs/1810.08280)
      4) DOS Header Attacks (example paper link: https://arxiv.org/abs/1901.03583)
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docstr: Randomly append bytes at the end of the malware to initialise it, or if perturbation regions are provided,
  perturb those.
  
  :param x: Array with input data.
  :param y: Labels, after having been adjusted to account for malware which cannot support the full l0 budget.
  :param sample_sizes: The size of the original file, before it was padded to the input size required by MalConv
  :param perturbation_size: Size of the perturbations in L0 terms to put at end of file
  :param perturb_sizes: List of length batch size, each element is in itself a list containing the size
                        of the allowable perturbation region
  :param perturb_starts: List of length batch size, each element is in itself a list containing the start
                         of perturbation region.
  :return x: Array with features to be perturbed set to a random value.
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code: def initialise_sample(
      x: np.ndarray,
      y: np.ndarray,
      sample_sizes: np.ndarray,
      perturbation_size: np.ndarray,
      perturb_sizes: Optional[List[List[int]]],
      perturb_starts: Optional[List[List[int]]],
  ) -> np.ndarray:
      """
      Randomly append bytes at the end of the malware to initialise it, or if perturbation regions are provided,
      perturb those.
  
      :param x: Array with input data.
      :param y: Labels, after having been adjusted to account for malware which cannot support the full l0 budget.
      :param sample_sizes: The size of the original file, before it was padded to the input size required by MalConv
      :param perturbation_size: Size of the perturbations in L0 terms to put at end of file
      :param perturb_sizes: List of length batch size, each element is in itself a list containing the size
                            of the allowable perturbation region
      :param perturb_starts: List of length batch size, each element is in itself a list containing the start
                             of perturbation region.
      :return x: Array with features to be perturbed set to a random value.
      """
      for j in range(len(x)):
          if y[j] == 1:
              if (
                  perturb_sizes is not None
                  and perturb_starts is not None
              ):
                  for size, start in zip(
                      perturb_sizes[j], perturb_starts[j]
                  ):
                      x[j, start : start + size] = np.random.randint(
                          low=0, high=256, size=(1, size)
                      )
              x[
                  j,
                  sample_sizes[j] : sample_sizes[j]
                  + perturbation_size[j],
              ] = np.random.randint(
                  low=0, high=256, size=(1, perturbation_size[j])
              )
  
      return x
  
imports: import json
  import random
  import logging
  from typing import Optional, Union, Tuple, List, Dict, TYPE_CHECKING
  from tqdm.auto import trange
  import numpy
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from art.attacks.attack import EvasionAttack
label_desc: Use image node_id 3 for calling the MalwareGDTensorFlow obj's underlying member method code with example usage: obj.initialise_sample(x, y, sample_sizes, perturbation_size, perturb_sizes, perturb_starts) and returns: x