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
# -*- coding: utf-8 -*-

'''
  Copyright(c) 2018, LiuYang
  All rights reserved.
  2018/02/23
'''
import tarfile
from six.moves import urllib
import sys
import numpy as np
import pickle
import os
import cv2

data_dir = 'cifar10_data'
full_data_dir = 'cifar10_data/cifar-10-batches-py/data_batch_'
vali_dir = 'cifar10_data/cifar-10-batches-py/test_batch'
DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'


IMG_WIDTH = 32
IMG_HEIGHT = 32
IMG_DEPTH = 3
NUM_CLASS = 10

TRAIN_RANDOM_LABEL = False # Want to use random label for train data?
VALI_RANDOM_LABEL = False # Want to use random label for validation?

NUM_TRAIN_BATCH = 5 # How many batches of files you want to read in, from 0 to 5)
EPOCH_SIZE = 10000 * NUM_TRAIN_BATCH


def maybe_download_and_extract():
    '''
    Will download and extract the cifar10 data automatically
    :return: nothing
    '''
    dest_directory = data_dir
    if not os.path.exists(dest_directory):
        os.makedirs(dest_directory)
    filename = DATA_URL.split('/')[-1]
    filepath = os.path.join(dest_directory, filename)
    if not os.path.exists(filepath):
        def _progress(count, block_size, total_size):
            sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename, float(count * block_size)
                                                             / float(total_size) * 100.0))
            sys.stdout.flush()
        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
        print()
        statinfo = os.stat(filepath)
        print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
        tarfile.open(filepath, 'r:gz').extractall(dest_directory)




def read_in_all_images(filename, shuffle=True):
    '''
    This function reads all training or validation data filepath
    :param filename: path of image files
    :param shuffle:
    :return: numpy array of images path and labels. data and label are 1D arrays: [num_images]
    '''
    data = []
    label = []
    with open(filename, 'r') as f:
        for line in f.readlines():
            temp = [ele for ele in line.strip().split(',')]
            data.append(temp[0])
            label.append(int(temp[1]))
    data = np.array(data)
    label = np.array(label)
    num_images = len(label)
    if shuffle is True:
        print('Shuffling')
        order = np.random.permutation(num_images)
        data = data[order]
        label = label[order]
    return data, label




def horizontal_flip(image, axis):
    '''
    Flip an image at 50% possibility
    :param image: a 3 dimensional numpy array representing an image
    :param axis: 0 for vertical flip and 1 for horizontal flip
    :return: 3D image after flip
    '''
    flip_prop = np.random.randint(low=0, high=2)
    if flip_prop == 0:
        image = cv2.flip(image, axis)

    return image


def whitening_image(image_np):
    '''
    Performs per_image_whitening
    :param image_np: a 4D numpy array representing a batch of images
    :return: the image numpy array after whitened
    '''
    for i in range(len(image_np)):
        mean = np.mean(image_np[i, ...])
        # Use adjusted standard deviation here, in case the std == 0.
        std = np.max([np.std(image_np[i, ...]), 1.0/np.sqrt(IMG_HEIGHT * IMG_WIDTH * IMG_DEPTH)])
        image_np[i,...] = (image_np[i, ...] - mean) / std
    return image_np


def random_crop_and_flip(batch_data, padding_size):
    '''
    Helper to random crop and random flip a batch of images
    :param padding_size: int. how many layers of 0 padding was added to each side
    :param batch_data: a 4D batch array
    :return: randomly cropped and flipped image
    '''
    cropped_batch = np.zeros(len(batch_data) * IMG_HEIGHT * IMG_WIDTH * IMG_DEPTH).reshape(
        len(batch_data), IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH)

    for i in range(len(batch_data)):
        x_offset = np.random.randint(low=0, high=2 * padding_size, size=1)[0]
        y_offset = np.random.randint(low=0, high=2 * padding_size, size=1)[0]
        cropped_batch[i, ...] = batch_data[i, ...][x_offset:x_offset+IMG_HEIGHT,
                      y_offset:y_offset+IMG_WIDTH, :]

        cropped_batch[i, ...] = horizontal_flip(image=cropped_batch[i, ...], axis=1)

    return cropped_batch









```

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

  A) CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
  B) CWE-918: Server-Side Request Forgery (SSRF)
  C) CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
  D) CWE-730: OWASP Top Ten 2004 Category A9 - Denial of Service
  E) CWE-215: Insertion of Sensitive Information Into Debugging Code
  F) CWE-611: Improper Restriction of XML External Entity Reference
Answer: C