from dataclasses import dataclass
from typing import Dict, Any, Optional
import torch

@dataclass
class MobileDeviceMetrics:
    battery_level: float
    available_memory: float
    cpu_usage: float
    network_strength: float

class MobileNode:
    def __init__(self, device_id: str):
        self.device_id = device_id
        self.model: Optional[torch.nn.Module] = None
        self.metrics = MobileDeviceMetrics(1.0, 1.0, 0.0, 1.0)
    
    def update_metrics(self, metrics: Dict[str, float]) -> None:
        self.metrics = MobileDeviceMetrics(**metrics)
    
    def can_process_update(self) -> bool:
        return (self.metrics.battery_level > 0.2 and
                self.metrics.available_memory > 0.3 and
                self.metrics.network_strength > 0.4)
    
    async def process_model_update(self, 
                                 update: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        if not self.can_process_update():
            return None
            
        try:
            # Process model update with resource constraints
            processed_update = self._apply_update(update)
            return processed_update
        except Exception:
            return None
            
    def _apply_update(self, update: Dict[str, Any]) -> Dict[str, Any]:
        # Implementation of resource-aware model update
        pass
