class_docstr: The librosa cache manager class wraps joblib.Memory
  with a __call__ attribute, so that it may act as a function.
  
  Additionally, it provides a caching level filter, so that
  different functions can be cached or not depending on the user's
  preference for speed vs. storage usage.
class_docstr_tok: ["The","librosa","cache","manager","class","wraps","joblib.Memory","with","a","__call__","attribute",",","so","that","it","may","act","as","a","function",".","Additionally",",","it","provides","a","caching","level","filter",",","so","that","different","functions","can","be","cached","or","not","depending","on","the","user's","preference","for","speed","vs.","storage","usage","."]
docstr: Reduce the size of the cache
docstr_tok: ["Reduce","the","size","of","the","cache"]
code: def reduce_size(self, *args: Any, **kwargs: Any) -> None:
      """Reduce the size of the cache"""
      self.memory.reduce_size(*args, **kwargs)
  
file_path: librosa/librosa/_cache.py
imports: import os
  from typing import Any, Callable, TypeVar
  from joblib import Memory
  from decorator import FunctionMaker
label_desc: Use image node_id 6 for calling the CacheManager obj's underlying member method code with example usage: obj.reduce_size() without return types