What are the best practices for handling Serverless Inference errors?

import openai

try:
    response = client.chat.completions.create(
        model="meta-llama/Llama-3.1-8B-Instruct",
        messages=messages
    )
except Exception as e:
    print(f"Error: {e}")
    # Handle error appropriately

import time
from typing import Optional

def call_inference_with_retry(
    client, 
    messages, 
    model: str,
    max_retries: int = 3,
    base_delay: float = 1.0
) -> Optional[str]:
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model=model,
                messages=messages
            )
            return response.choices[0].message.content
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            
            # Calculate delay with exponential backoff
            delay = base_delay * (2 ** attempt)
            print(f"Attempt {attempt + 1} failed, retrying in {delay}s...")
            time.sleep(delay)
    
    return None

def handle_inference_error(error):
    error_str = str(error)
    
    if "401" in error_str:
        # Invalid authentication
        raise ValueError("Check your API key and project configuration")
    elif "402" in error_str:
        # Out of credits
        raise ValueError("Insufficient credits")
    elif "429" in error_str:
        # Rate limited
        return "retry"
    elif "500" in error_str or "503" in error_str:
        # Server error
        return "retry"
    else:
        # Unknown error
        raise

# For longer responses
client = openai.OpenAI(
    base_url='https://api.inference.wandb.ai/v1',
    api_key="your-api-key",
    timeout=60.0  # 60 second timeout
)