class_docstr: ## Cache
  
  This maintains a key-value cache and queues push values and pop them in the same order.
  The queues are useful since we have multiple attention layers.
class_docstr_tok: ["#","#","Cache","This","maintains","a","key-value","cache","and","queues","push","values","and","pop","them","in","the","same","order",".","The","queues","are","useful","since","we","have","multiple","attention","layers","."]
docstr: ### Cache a value
  
  :param key: is the name of the value to be cached
  :param value: is the value
docstr_tok: ["#","#","#","Cache","a","value",":","param","key",":","is","the","name","of","the","value","to","be","cached",":","param","value",":","is","the","value"]
code: def set(self, key: str, value: Any):
      """
      ### Cache a value
  
      :param key: is the name of the value to be cached
      :param value: is the value
      """
      self._cache[key] = value
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/neox/utils/cache.py
imports: from typing import Any
label_desc: Use image node_id 6 for calling the Cache obj's underlying member method code with example usage: obj.set(key, value) without return types