system: You are an expert security code reviewer. You will be shown a source code file along with a multiple-choice question listing several function or class names from that file. Your task is to identify which one contains the specified CWE vulnerability.

OUTPUT FORMAT: Respond with the single letter of the correct option, wrapped in \boxed{...}. Example: `\boxed{B}`. Output the boxed letter ONLY -- no explanation, no other text.

user: ```python
import io
import json
from PIL import Image
from flask import Flask, jsonify, request
import hashlib
import yaml

import torch
from torchvision import models
import torchvision.transforms as transforms

from ai.utils.model import CustomModel
import os

from waitress import serve


app = Flask(__name__)


@app.route('/')
def hello():
    hello = "Hello API"
    return hello


# AI Models
print(f"Loading model ... ", end='')
model = CustomModel()
model.eval()
device = 'cpu'
print(f"Done!!")


def to_RGB(image: Image, file_name='./tmp/tmp.jpg'):

    os.makedirs('./tmp/', exist_ok=True)

    if image.mode == 'RGB':
        return image
    image.load()  # required for png.split()
    background = Image.new("RGB", image.size, (255, 255, 255))
    background.paste(image, mask=image.split()[3])  # 3 is the alpha channel

    background.save(file_name, 'JPEG', quality=80)
    return Image.open(file_name)


def api_auth(api_key):
    hash_key = hashlib.sha256(api_key.encode('utf-8')).hexdigest()
    with open('ai/api_true_hash_key.yaml', 'r') as f:
        secret = yaml.safe_load(f)
    true_hash_key = secret['AI']['API_TRUE_HASH_KEY']
    if true_hash_key == hash_key:
        return True
    else:
        return False


def transform_image(image_bytes):
    my_transforms = transforms.Compose([transforms.Resize((512, 512)),
                                        transforms.ToTensor(),
                                        transforms.Normalize(
                                            [0.485, 0.456, 0.406],
                                            [0.229, 0.224, 0.225])])
    image = Image.open(io.BytesIO(image_bytes))
    image = to_RGB(image)
    return my_transforms(image).unsqueeze(0)


def get_vector(tensor):
    print(f"Predicting ... ", end='')
    with torch.no_grad():
        out = model(tensor)
    print(f"Done!!")
    out = out.to('cpu').detach().numpy().tolist()[0]
    return out


@app.route('/predict', methods=['POST'])
def predict():
    if request.method == 'POST':
        file = request.files['file']
        if api_auth(request.headers.get('api_key')):
            image_bytes = file.read()
            tensor = transform_image(image_bytes=image_bytes)
            tensor = tensor.to(device)
            vector = get_vector(tensor)
            return jsonify({'authentication': 'ok', 'vector': vector})
        else:
            return jsonify({'authentication': 'no'})


def main():
    app.run(debug=True, port=7775)
    # serve(app, host='0.0.0.0', port=7775)


if __name__ == "__main__":
    main()

```

Which of the following functions or classes contains a `CWE-327: Use of a Broken or Risky Cryptographic Algorithm` vulnerability?

  A) predict
  B) to_RGB
  C) main
  D) api_auth
Answer: D