code: def update_cache(
      type: Optional[Literal["local", "redis"]] = "local",
      host: Optional[str] = None,
      port: Optional[str] = None,
      password: Optional[str] = None,
      supported_call_types: Optional[
          List[
              Literal[
                  "completion", "acompletion", "embedding", "aembedding"
              ]
          ]
      ] = ["completion", "acompletion", "embedding", "aembedding"],
      **kwargs,
  ):
      """
      Update the cache for LiteLLM.
  
      Args:
          type (Optional[Literal["local", "redis"]]): The type of cache. Defaults to "local".
          host (Optional[str]): The host of the cache. Defaults to None.
          port (Optional[str]): The port of the cache. Defaults to None.
          password (Optional[str]): The password for the cache. Defaults to None.
          supported_call_types (Optional[List[Literal["completion", "acompletion", "embedding", "aembedding"]]]):
              The supported call types for the cache. Defaults to ["completion", "acompletion", "embedding", "aembedding"].
          **kwargs: Additional keyword arguments for the cache.
  
      Returns:
          None
  
      """
      print_verbose("LiteLLM: Updating Cache")
      litellm.cache = Cache(
          type=type,
          host=host,
          port=port,
          password=password,
          supported_call_types=supported_call_types,
          **kwargs,
      )
      print_verbose(
          f"LiteLLM: Cache Updated, litellm.cache={litellm.cache}"
      )
      print_verbose(f"LiteLLM Cache: {vars(litellm.cache)}")
  
imports: import litellm
  import time, logging
  import json, traceback, ast, hashlib
  from typing import Optional, Literal, List, Union, Any
  from openai._models import BaseModel
label_desc: Use image node_id 3 for calling a global function with example usage: update_cache(type, host, port, password, supported_call_types) without return types