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)
class_docstr_tok: ["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",")"]
docstr: Go from raw file to numpy array.
  
  :param filepath: Path to the file we convert to a numpy array
  :param padding_char: The char to use to pad the input if it is shorter then maxlen
  :param maxlen: Maximum size of the file processed by the model. Currently set to 1MB
  :return data: A numpy array of the PE file
  :return size_of_original_file: Size of the PE file
docstr_tok: ["Go","from","raw","file","to","numpy","array",".",":","param","filepath",":","Path","to","the","file","we","convert","to","a","numpy","array",":","param","padding_char",":","The","char","to","use","to","pad","the","input","if","it","is","shorter","then","maxlen",":","param","maxlen",":","Maximum","size","of","the","file","processed","by","the","model",".","Currently","set","to","1MB",":","return","data",":","A","numpy","array","of","the","PE","file",":","return","size_of_original_file",":","Size","of","the","PE","file"]
code: def process_file(
      filepath: str, padding_char: int = 256, maxlen: int = 2**20
  ) -> Tuple[np.ndarray, int]:  # pragma: no cover
      """
      Go from raw file to numpy array.
  
      :param filepath: Path to the file we convert to a numpy array
      :param padding_char: The char to use to pad the input if it is shorter then maxlen
      :param maxlen: Maximum size of the file processed by the model. Currently set to 1MB
      :return data: A numpy array of the PE file
      :return size_of_original_file: Size of the PE file
      """
      with open(filepath, "rb") as file:
          open_file = file.read()
      size_of_original_file = len(open_file)
  
      data = np.ones((maxlen,), dtype=np.uint16) * padding_char
      selected_bytes = np.frombuffer(open_file[:maxlen], dtype=np.uint8)
      data[: len(selected_bytes)] = selected_bytes
  
      return data, size_of_original_file
  
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 12 for calling the MalwareGDTensorFlow obj's underlying member method code with example usage: obj.process_file(filepath, padding_char, maxlen) and returns: data,  size_of_original_file