system: You are an expert security code reviewer. You will be shown a source code file along with a catalog of candidate CWE (Common Weakness Enumeration) categories with their official MITRE names. Your task is to list every distinct CWE from that catalog that is actually present in the code. The catalog is comprehensive for this dataset -- the answer is a (possibly small) subset of it.

OUTPUT FORMAT: Respond with a comma-separated list of CWE IDs in the form `CWE-N`, sorted in ascending numeric order, wrapped in a single \boxed{...}. Examples: `\boxed{CWE-79}` for one, `\boxed{CWE-22,CWE-79,CWE-327}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
import re
import numpy as np

import nltk
nltk.download("stopwords")
from nltk.corpus import stopwords

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences

class DataPreprocessing:
    def __init__(self):
        pass
    
    def clean_data(self, dataset):
        # -- Convert to lower case
        dataset["Tweet"] = dataset["Tweet"].apply(lambda x: x.lower())

        # -- Remove mentions
        dataset['Tweet'] = dataset['Tweet'].apply(lambda x : ' '.join([y for y in x.split() if not y.startswith('@')])) 

        # -- Remove links
        dataset['Tweet'] = dataset['Tweet'].apply(lambda x : ' '.join([y for y in x.split() if not y.startswith('http')])) 

        # -- Remove hashtags
        dataset['Tweet'] = dataset['Tweet'].apply(lambda x : ' '.join([y for y in x.split() if not y.startswith('#')])) 

        # -- Remove weird characters
        dataset["Tweet"] = dataset["Tweet"].apply((lambda x: re.sub('[^a-zA-z0-9\s]', '', x)))

        # -- Remove AlphaNumeric Words
        dataset["Tweet"] = dataset["Tweet"].apply(lambda x: re.sub('\S*\d\S*', '', x).strip())

        # -- Remove Stopwords
        stop_words = set(stopwords.words('english'))
        dataset["Tweet"] = dataset["Tweet"].apply(lambda x: ' '.join(word for word in x.split() if word not in stop_words))

        return dataset

    def tokenize_and_pad_data(self, sequences, max_len, alt_sequences=None):
        """
        Description:
            Transforms word sequences to padded integer
            sequences

        Attributes:
            - sequences(numpy array): Dataset to transform.
            e.g: sequences = my_df["my_column"]

            - max_len(int): max length for padding.

            - alt_sequences(numpy array): Feed the tokenizer with
            the data of another dataset.
        """
        if alt_sequences is None:
            return self.tok_and_pad(sequences, max_len)
        else:
            self.tokenizer = Tokenizer(lower = True, filters='')
            self.tokenizer.fit_on_texts(alt_sequences.values)

            # Text to integer
            tokenized_seq = self.tokenizer.texts_to_sequences(sequences)

            # Return padded data
            return pad_sequences(tokenized_seq, maxlen = max_len)

    def tok_and_pad(self, sequences, max_len):
        """
        Description:
            - Auxiliar method of tokenize_and_pad_data.
        """
        self.tokenizer = Tokenizer(lower = True, filters='')
        self.tokenizer.fit_on_texts(sequences.values)

        # Text to integer
        tokenized_seq = self.tokenizer.texts_to_sequences(sequences)

        # Return padded data
        return pad_sequences(tokenized_seq, maxlen = max_len)

    def get_coefs(self, word,*arr):
        return word, np.asarray(arr, dtype='float32')

    def get_embedding_index(self,embedding_file):
        return dict(self.get_coefs(*o.strip().split(" ")) for o in open(embedding_file))

    def create_embedding_matrix(self, vocab_size, embedding_size, word_index, embedding_index):
        embedding_matrix = np.zeros((vocab_size + 1, embedding_size))

        for word, i in word_index.items():
            if i >= vocab_size:
                continue
            embedding_vector = embedding_index.get(word)

            if embedding_vector is not None:
                embedding_matrix[i] = embedding_vector

        return embedding_matrix







```

Candidate CWE catalog (every CWE that appears anywhere in this dataset):
  - CWE-20: Improper Input Validation
  - CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
  - CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
  - CWE-78: Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
  - CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
  - CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
  - CWE-94: Improper Control of Generation of Code ('Code Injection')
  - CWE-113: Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')
  - CWE-116: Improper Encoding or Escaping of Output
  - CWE-117: Improper Output Neutralization for Logs
  - CWE-200: Exposure of Sensitive Information to an Unauthorized Actor
  - CWE-209: Generation of Error Message Containing Sensitive Information
  - CWE-215: Insertion of Sensitive Information Into Debugging Code
  - CWE-295: Improper Certificate Validation
  - CWE-312: Cleartext Storage of Sensitive Information
  - CWE-326: Inadequate Encryption Strength
  - CWE-327: Use of a Broken or Risky Cryptographic Algorithm
  - CWE-377: Insecure Temporary File
  - CWE-502: Deserialization of Untrusted Data
  - CWE-601: URL Redirection to Untrusted Site ('Open Redirect')
  - CWE-611: Improper Restriction of XML External Entity Reference
  - CWE-614: Sensitive Cookie in HTTPS Session Without 'Secure' Attribute
  - CWE-643: Improper Neutralization of Data within XPath Expressions ('XPath Injection')
  - CWE-730: OWASP Top Ten 2004 Category A9 - Denial of Service
  - CWE-732: Incorrect Permission Assignment for Critical Resource
  - CWE-776: Improper Restriction of Recursive Entity References in DTDs ('XML Entity Expansion')
  - CWE-918: Server-Side Request Forgery (SSRF)
  - CWE-943: Improper Neutralization of Special Elements in Data Query Logic
  - CWE-1004: Sensitive Cookie Without 'HttpOnly' Flag
  - CWE-1333: Inefficient Regular Expression Complexity

List every distinct CWE from the catalog above that is actually present in this code.
Answer: CWE-20