system: You are an expert security code reviewer. You will be shown a source code file along with a multi-select question listing several CWE (Common Weakness Enumeration) categories. Your task is to identify which of the listed CWEs are present in the code.

OUTPUT FORMAT: Respond with the letter(s) of all correct options, sorted alphabetically and comma-separated, wrapped in a single \boxed{...}. Examples: `\boxed{A}` for one correct, `\boxed{A,C}` for two, `\boxed{A,C,E}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
""" A neural chatbot using sequence to sequence model with
attentional decoder. 

This is based on Google Translate Tensorflow model 
https://github.com/tensorflow/models/blob/master/tutorials/rnn/translate/

Sequence to sequence model by Cho et al.(2014)

Created by Chip Huyen as the starter code for assignment 3,
class CS 20SI: "TensorFlow for Deep Learning Research"
cs20si.stanford.edu

This file contains the code to do the pre-processing for the
Cornell Movie-Dialogs Corpus.

See readme.md for instruction on how to run the starter code.
"""
from __future__ import print_function

import os
import random
import re

import numpy as np

from windbag import config


def get_lines():
  id2line = {}
  file_path = os.path.join(config.DATA_PATH, config.LINE_FILE)
  with open(file_path, 'rb') as f:
    lines = f.readlines()
    for line in lines:
      parts = line.split(b' +++$+++ ')
      if len(parts) == 5:
        if parts[4][-1] == b'\n'[0]:
          parts[4] = parts[4][:-1]
        id2line[parts[0]] = parts[4]
  return id2line


def get_convos():
  """ Get conversations from the raw data """
  file_path = os.path.join(config.DATA_PATH, config.CONVO_FILE)
  convos = []
  with open(file_path, 'rb') as f:
    for line in f.readlines():
      parts = line.split(b' +++$+++ ')
      if len(parts) == 4:
        convo = []
        for line in parts[3][1:-2].split(b', '):
          convo.append(line[1:-1])
        convos.append(convo)

  return convos


def question_answers(id2line, convos):
  """ Divide the dataset into two sets: questions and answers. """
  questions, answers = [], []
  for convo in convos:
    for index, line in enumerate(convo[:-1]):
      questions.append(id2line[convo[index]])
      answers.append(id2line[convo[index + 1]])
  assert len(questions) == len(answers)
  return questions, answers


def prepare_dataset(questions, answers):
  # create path to store all the train & test encoder & decoder
  make_dir(config.PROCESSED_PATH)

  # random convos to create the test set
  test_ids = random.sample([i for i in range(len(questions))], config.TESTSET_SIZE)

  filenames = ['train.enc', 'train.dec', 'test.enc', 'test.dec']
  files = []
  for filename in filenames:
    files.append(open(os.path.join(config.PROCESSED_PATH, filename), 'wb'))

  print('going to write {} questions'.format(len(questions)))
  for i in range(len(questions)):
    if i in test_ids:
      files[2].write(questions[i] + b'\n')
      files[3].write(answers[i] + b'\n')
    else:
      files[0].write(questions[i] + b'\n')
      files[1].write(answers[i] + b'\n')

  for file in files:
    file.close()


def make_dir(path):
  """ Create a directory if there isn't one already. """
  try:
    os.mkdir(path)
  except OSError:
    pass


def basic_tokenizer(line, normalize_digits=True):
  """ A basic tokenizer to tokenize text into tokens.
  Feel free to change this to suit your need. """
  line = re.sub(b'<u>', b'', line)
  line = re.sub(b'</u>', b'', line)
  line = re.sub(br'\[', b'', line)
  line = re.sub(br'\]', b'', line)
  words = []
  _WORD_SPLIT = re.compile(b"([.,!?\"'-<>:;)(])")
  _DIGIT_RE = re.compile(br"\d")
  for fragment in line.strip().lower().split():
    for token in re.split(_WORD_SPLIT, fragment):
      if not token:
        continue
      if normalize_digits:
        token = re.sub(_DIGIT_RE, b'#', token)
      words.append(token)
  return words


def build_vocab(filename, normalize_digits=True):
  in_path = os.path.join(config.PROCESSED_PATH, filename)
  out_path = os.path.join(config.PROCESSED_PATH, 'vocab.{}'.format(filename[-3:]))

  vocab = {}
  with open(in_path, 'rb') as f:
    for line in f.readlines():
      for token in basic_tokenizer(line):
        if not token in vocab:
          vocab[token] = 0
        vocab[token] += 1

  sorted_vocab = sorted(vocab, key=vocab.get, reverse=True)
  with open(out_path, 'wb') as f:
    f.write(b'<pad>' + b'\n')
    f.write(b'<unk>' + b'\n')
    f.write(b'
' + b'\n')
    f.write(b'
' + b'\n')
    index = 4
    for word in sorted_vocab:
      if vocab[word] < config.THRESHOLD:
        with open('windbag/config.py', 'ab') as cf:
          if filename[-3:] == 'enc':
            cf.write(b'ENC_VOCAB = ' + str(index).encode('ascii') + b'\n')
          else:
            cf.write(b'DEC_VOCAB = ' + str(index).encode('ascii') + b'\n')
        break
      f.write(word + b'\n')
      index += 1


def load_vocab(vocab_path):
  with open(vocab_path, 'rb') as f:
    words = f.read().splitlines()
  return words, {words[i]: i for i in range(len(words))}


def sentence2id(vocab, line):
  return [vocab.get(token, vocab[b'<unk>']) for token in basic_tokenizer(line)]


def token2id(data, mode):
  """ Convert all the tokens in the data into their corresponding
  index in the vocabulary. """
  vocab_path = 'vocab.' + mode
  in_path = data + '.' + mode
  out_path = data + '_ids.' + mode
  _, vocab = load_vocab(os.path.join(config.PROCESSED_PATH, vocab_path))
  in_file = open(os.path.join(config.PROCESSED_PATH, in_path), 'rb')
  out_file = open(os.path.join(config.PROCESSED_PATH, out_path), 'wb')

  lines = in_file.read().splitlines()
  for line in lines:
    if mode == 'dec':  # we only care about '
' and 
 in encoder
      ids = [vocab[b'
']]
    else:
      ids = []
    ids.extend(sentence2id(vocab, line))
    # ids.extend([vocab.get(token, vocab['<unk>']) for token in basic_tokenizer(line)])
    if mode == 'dec':
      ids.append(vocab[b'
'])
    out_file.write(b' '.join(str(id_).encode('ascii') for id_ in ids) + b'\n')


def prepare_raw_data():
  print('Preparing raw data into train set and test set ...')
  id2line = get_lines()
  convos = get_convos()
  questions, answers = question_answers(id2line, convos)
  prepare_dataset(questions, answers)


def process_data():
  print('Preparing data to be model-ready ...')
  build_vocab('train.enc')
  build_vocab('train.dec')
  token2id('train', 'enc')
  token2id('train', 'dec')
  token2id('test', 'enc')
  token2id('test', 'dec')


def load_data(enc_filename, dec_filename, max_training_size=None):
  encode_file = open(os.path.join(config.PROCESSED_PATH, enc_filename), 'rb')
  decode_file = open(os.path.join(config.PROCESSED_PATH, dec_filename), 'rb')
  encode, decode = encode_file.readline(), decode_file.readline()
  encode = encode.decode('ascii')
  decode = decode.decode('ascii')
  data_buckets = [[] for _ in config.BUCKETS]
  i = 0
  while encode and decode:
    if (i + 1) % 10000 == 0:
      print("Bucketing conversation number", i)
    encode_ids = [int(id_) for id_ in encode.split()]
    decode_ids = [int(id_) for id_ in decode.split()]
    for bucket_id, (encode_max_size, decode_max_size) in enumerate(config.BUCKETS):
      if len(encode_ids) <= encode_max_size and len(decode_ids) <= decode_max_size:
        data_buckets[bucket_id].append([encode_ids, decode_ids])
        break
    encode, decode = encode_file.readline(), decode_file.readline()
    encode = encode.decode('ascii')
    decode = decode.decode('ascii')
    i += 1
  return data_buckets


def _pad_input(input_, size):
  return input_ + [config.PAD_ID] * (size - len(input_))


def _reshape_batch(inputs, size, batch_size):
  """ Create batch-major inputs. Batch inputs are just re-indexed inputs
  """
  batch_inputs = []
  for length_id in range(size):
    batch_inputs.append(np.array([inputs[batch_id][length_id]
                                  for batch_id in range(batch_size)], dtype=np.int32))
  return batch_inputs


def get_batch(data_bucket, bucket_id, batch_size=1):
  """ Return one batch to feed into the model """
  # only pad to the max length of the bucket
  encoder_size, decoder_size = config.BUCKETS[bucket_id]
  encoder_inputs, decoder_inputs = [], []

  for _ in range(batch_size):
    encoder_input, decoder_input = random.choice(data_bucket)
    # pad both encoder and decoder, reverse the encoder
    encoder_inputs.append(list(reversed(_pad_input(encoder_input, encoder_size))))
    decoder_inputs.append(_pad_input(decoder_input, decoder_size))

  # now we create batch-major vectors from the data selected above.
  batch_encoder_inputs = _reshape_batch(encoder_inputs, encoder_size, batch_size)
  batch_decoder_inputs = _reshape_batch(decoder_inputs, decoder_size, batch_size)

  # create decoder_masks to be 0 for decoders that are padding.
  batch_masks = []
  for length_id in range(decoder_size):
    batch_mask = np.ones(batch_size, dtype=np.float32)
    for batch_id in range(batch_size):
      # we set mask to 0 if the corresponding target is a PAD symbol.
      # the corresponding decoder is decoder_input shifted by 1 forward.
      if length_id < decoder_size - 1:
        target = decoder_inputs[batch_id][length_id + 1]
      if length_id == decoder_size - 1 or target == config.PAD_ID:
        batch_mask[batch_id] = 0.0
    batch_masks.append(batch_mask)
  return batch_encoder_inputs, batch_decoder_inputs, batch_masks


if __name__ == '__main__':
  prepare_raw_data()
  process_data()

```

Which of the following CWE vulnerability categories are present in this code? Multiple options may be correct.

  A) CWE-918: Server-Side Request Forgery (SSRF)
  B) CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
  C) CWE-20: Improper Input Validation
  D) CWE-1004: Sensitive Cookie Without 'HttpOnly' Flag
  E) CWE-611: Improper Restriction of XML External Entity Reference
  F) CWE-327: Use of a Broken or Risky Cryptographic Algorithm
Answer: C