code: def __init__(
      self,
      *,
      api_key=None,
      organization: Optional[str] = None,
      base_url: Optional[str] = None,
      timeout: Optional[float] = 600,
      max_retries: Optional[int] = litellm.num_retries,
      default_headers: Optional[Mapping[str, str]] = None,
  ):
      self.params = locals()
      self.chat = Chat(self.params)
  
imports: import os, openai, sys, json, inspect, uuid, datetime, threading
  from typing import Any, Literal, Union
  from functools import partial
  import dotenv, traceback, random, asyncio, time, contextvars
  from copy import deepcopy
  import httpx
  import litellm
  from litellm import client, exception_type, get_optional_params, get_litellm_params, Logging
  from litellm.utils import get_secret, CustomStreamWrapper, read_config_args, completion_with_fallbacks, get_llm_provider, get_api_key, mock_completion_streaming_obj, convert_to_model_response_object, token_counter, Usage, get_optional_params_embeddings, get_optional_params_image_gen
  from .llms import anthropic, together_ai, ai21, sagemaker, bedrock, huggingface_restapi, replicate, aleph_alpha, nlp_cloud, baseten, vllm, ollama, ollama_chat, cloudflare, cohere, petals, oobabooga, openrouter, palm, gemini, vertex_ai, maritalk
  from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion
  from .llms.azure import AzureChatCompletion
  from .llms.huggingface_restapi import Huggingface
  from .llms.prompt_templates.factory import prompt_factory, custom_prompt, function_call_prompt
  import tiktoken
  from concurrent.futures import ThreadPoolExecutor
  from typing import Callable, List, Optional, Dict, Union, Mapping
  from .caching import enable_cache, disable_cache, update_cache
  from litellm.utils import get_secret, CustomStreamWrapper, TextCompletionStreamWrapper, ModelResponse, TextCompletionResponse, TextChoices, EmbeddingResponse, read_config_args, Choices, Message
label_desc: Use image node_id 1 to create a new LiteLLM object  with example: obj = LiteLLM()