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
import re
import string

import numpy as np
import pandas as pd
from nltk.tokenize import word_tokenize
from sklearn.model_selection import train_test_split
from tensorflow.keras.initializers import Constant
from tensorflow.keras.layers import Embedding, LSTM, Dense, SpatialDropout1D
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam, RMSprop
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
from tqdm import tqdm

# %%
train_file = "/Users/brianelinsky/Dropbox/ActiveProjects/modeling_projects/disaster_tweets/data/2020-05-25/train.csv"
test_file = "/Users/brianelinsky/Dropbox/ActiveProjects/modeling_projects/disaster_tweets/data/2020-05-25/test.csv"

train = pd.read_csv(train_file)
test = pd.read_csv(test_file)

# Temporarily combine to clean the data
df = pd.concat([train, test])


# Write functions to clean the tweets
def remove_URLs(text):
    url = re.compile(r'https?://\S+|www\.\S+')
    return url.sub(r'', text)


def remove_html(text):
    html = re.compile(r'<.*?>')
    return html.sub(r'', text)


def remove_emoji(text):
    emoji_pattern = re.compile("["
                               u"\U0001F600-\U0001F64F"  # emoticons
                               u"\U0001F300-\U0001F5FF"  # symbols & pictographs
                               u"\U0001F680-\U0001F6FF"  # transport & map symbols
                               u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
                               u"\U00002702-\U000027B0"
                               u"\U000024C2-\U0001F251"
                               "]+", flags=re.UNICODE)
    return emoji_pattern.sub(r'', text)


def remove_punct(text):
    table = str.maketrans('', '', string.punctuation)
    return text.translate(table)


# Clean the test and train data
df['text'] = df['text'].apply(lambda x: remove_URLs(x))
df['text'] = df['text'].apply(lambda x: remove_html(x))
df['text'] = df['text'].apply(lambda x: remove_emoji(x))
df['text'] = df['text'].apply(lambda x: remove_punct(x))


# Takes a sentence, and converts it to a list of words
def create_corpus(df):
    corpus = []
    for tweet in df['text']:
        words = [word.lower() for word in word_tokenize(tweet) if ((word.isalpha() == 1))]
        corpus.append(words)
    return corpus


corpus = create_corpus(df)

# Use glove to convert the words into vectors
embed_dict = {}
with open('/Users/brianelinsky/Downloads/glove/glove.6B.100d.txt', 'r') as file:
    for line in file:
        values = line.split()
        word = values[0]
        vectors = np.asarray(values[1:], 'float32')
        embed_dict[word] = vectors
file.close()

MAX_LENGTH = 50
tokenizer = Tokenizer()
tokenizer.fit_on_texts(corpus)
sequences = tokenizer.texts_to_sequences(corpus)

# Pad the sequences
tweet_padded = pad_sequences(sequences, maxlen=MAX_LENGTH, truncating='post', padding='post')

word_index = tokenizer.word_index
print('Number of unique words:', len(word_index))

num_words = len(word_index) + 1
embedding_matrix = np.zeros((num_words, 100))

for word, i in tqdm(word_index.items()):
    if i > num_words:
        continue

    emb_vec = embed_dict.get(word)
    if emb_vec is not None:
        embedding_matrix[i] = emb_vec

# Create first model.  This model will use the Adam optimizer
model = Sequential()

embedding = Embedding(num_words, 100, embeddings_initializer=Constant(embedding_matrix),
                      input_length=MAX_LENGTH, trainable=False)

model.add(embedding)
model.add(SpatialDropout1D(0.2))
model.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))

optimzer = Adam(learning_rate=1e-5)

model.compile(loss='binary_crossentropy', optimizer=optimzer, metrics=['accuracy'])

model.summary()

# Create second model.  This model will use the RMSprop optimizer instead
model2 = Sequential()

embedding = Embedding(num_words, 100, embeddings_initializer=Constant(embedding_matrix),
                      input_length=MAX_LENGTH, trainable=False)

model2.add(embedding)
model2.add(SpatialDropout1D(0.2))
model2.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2))
model2.add(Dense(1, activation='sigmoid'))

optimzer2 = RMSprop(learning_rate=1e-5)

model2.compile(loss='binary_crossentropy', optimizer=optimzer2, metrics=['accuracy'])

model2.summary()

train = tweet_padded[:train.shape[0]]
test = tweet_padded[train.shape[0]:]

# Split data into test and train sets
X_train, X_test, y_train, y_test = train_test_split(train, train['target'].values, test_size=0.15)
print('Shape of train', X_train.shape)
print("Shape of Validation ", X_test.shape)

# Train both models with 15 epochs
history = model.fit(X_train, y_train, batch_size=4, epochs=15, validation_data=(X_test, y_test), verbose=2)
history2 = model2.fit(X_train, y_train, batch_size=4, epochs=15, validation_data=(X_test, y_test), verbose=2)

sample_sub = pd.read_csv(
    '/Users/brianelinsky/Dropbox/ActiveProjects/modeling_projects/disaster_tweets/data/2020-05-25/sample_submission.csv')

# Generate predictions
y_pred = model.predict(test)
y_pred2 = model2.predict(test)
y_pred = np.round(y_pred).astype(int).reshape(3263)
y_pred2 = np.round(y_pred2).astype(int).reshape(3263)

# Create submissions
sub = pd.DataFrame({'id': sample_sub['id'].values.tolist(), 'target': y_pred})
sub2 = pd.DataFrame({'id': sample_sub['id'].values.tolist(), 'target': y_pred2})
sub.to_csv('submission.csv', index=False)
sub2.to_csv('submission2.csv', index=False)

sub.head()
sub2.head()

```

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

  A) CWE-1004: Sensitive Cookie Without 'HttpOnly' Flag
  B) CWE-117: Improper Output Neutralization for Logs
  C) CWE-611: Improper Restriction of XML External Entity Reference
  D) CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
  E) CWE-601: URL Redirection to Untrusted Site ('Open Redirect')
  F) CWE-20: Improper Input Validation
Answer: F