Patched code sample

import os
import hashlib
from pathlib import Path
from typing import Optional, Set
import onnx

def load_onnx_model(
    model_path: str,
    allowed_dir: str,
    expected_hash: Optional[str] = None,
    max_size: int = 100 * 1024 * 1024  # 100MB
) -> onnx.ModelProto:
    
    try:
        model_path = os.path.abspath(model_path)
        allowed_dir = os.path.abspath(allowed_dir)
        
        if not model_path.startswith(allowed_dir):
            raise ValueError("Model path outside allowed directory")
            
        if not os.path.isfile(model_path):
            raise ValueError("Model file not found")
            
        model_size = os.path.getsize(model_path)
        if model_size > max_size:
            raise ValueError(f"Model too large: {model_size} bytes")
            
        if expected_hash:
            with open(model_path, 'rb') as f:
                file_hash = hashlib.sha256(f.read()).hexdigest()
            if file_hash != expected_hash:
                raise ValueError("Model hash mismatch")
                
        model = onnx.load(model_path)
        
        onnx.checker.check_model(model)
        
        for tensor in model.graph.initializer:
            if tensor.HasField('data_location') and tensor.data_location == onnx.TensorProto.EXTERNAL:
                for entry in tensor.external_data:
                    if entry.key == 'location':
                        data_path = os.path.abspath(
                            os.path.join(os.path.dirname(model_path), entry.value)
                        )
                        if not data_path.startswith(allowed_dir):
                            raise ValueError(
                                f"External data path outside allowed directory: {entry.value}"
                            )
                            
        return model
        
    except onnx.checker.ValidationError as e:
        raise ValueError(f"Invalid ONNX model: {str(e)}")
    except Exception as e:
        raise ValueError(f"Error loading model: {str(e)}")