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
"""
IMDB Text Classification
AutoKeras

May 2019

Vocab from [Stanford](http://ai.stanford.edu/~amaas/data/sentiment/)

Preprocessed data from [Keras](https://keras.io/datasets/)
"""
import os
import re
import urllib
import zipfile
import tarfile
import numpy as np
import pandas as pd
import tensorflow as tf

from keras.datasets import imdb
from autokeras.utils import read_tsv_file
from autokeras.text.text_supervised import TextClassifier

def download_url_to_filepath(outpath, url):
    """Create path and download data from url.
    Args
    :outpath: str save to this path
    :url: str url/to/data
    :return: str outpath with data
    """
    fd, fn = re.findall(r"^(.+\/)([^\/]+)$",outpath)[0]
    fp = fd + fn
    if not os.path.exists(fd):
        os.makedirs(fd)
    if not os.path.exists(fp):
        urllib.request.urlretrieve(url, fp) 
    return fp 

def unzip_file(in_path,out_dir):
    """Decompress file (tar.gz or zip)
    Args
    :in_path: str path/to/compressed/file
    :out_dir: str directory with uncompressed folder
    :return: output directory
    """
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)
        if in_path[-3:]=="zip":
            z = zipfile.ZipFile(in_path,'r')
            z.extractall(out_dir)
            z.close()
        elif in_path[-6:]=="tar.gz":
            tar = tarfile.open(fp)
            tar.extractall(path=out_dir)
            tar.close()    
    return out_dir

def load_data(path):
    """Loads Keras dataset preprocessed into integers by frequency of occurrence, 1 being most frequent
    
    Args
    :path: str path/to/keras/dataset
    :return: tuple(arr,arr)  two tuples of train and test
    """
    (x_train, y_train), (x_test, y_test) = imdb.load_data(path=path)
    return (x_train, y_train), (x_test, y_test)

def convert_labels_to_one_hot(labels, num_labels):
    """One hot encode labels"""
    one_hot = np.zeros((len(labels), num_labels))
    one_hot[np.arange(len(labels)), labels] = 1
    return one_hot

def convert_int_to_word(integer):
    """Convert processed integer to word from vocab"""
    return vocab.values[integer][0]

def convert_int_to_str_array(array):
    return np.array([" ".join(map(str,[convert_int_to_word(n) for n in integer])) for integer in array])

fp = download_url_to_filepath("/tmp/imdb.tar.gz","http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz")
fd = unzip_file(fp,"/tmp/imdb")
vocab = pd.read_csv(os.path.join(fd,"imdb/aclImdb/imdb.vocab"))

(x_train, y_train), (x_test, y_test) = load_data(path="imdb.npz")
print(f"X,Y Train: {len(x_train),len(x_train[0])},{len(y_train)}")
print(f"X,Y Test: {len(x_test),len(x_test[0])},{len(y_test)}")

x_train, y_train = (x_train, y_train)
x_test, y_test = (x_test, y_test)

x_train = convert_int_to_str_array(x_train)
x_test = convert_int_to_str_array(x_test)

y_train = convert_labels_to_one_hot(y_train, num_labels=2)
y_test = convert_labels_to_one_hot(y_test, num_labels=2)

clf = TextClassifier(verbose=True)
clf.fit(x=x_train, y=y_train, time_limit=12 * 60 * 60)
print("Classification accuracy is : ", 100 * clf.evaluate(x_test, y_test), "%")


```

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-22