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: ### Clear a cache value
  
  :param key: is the name used when caching
docstr_tok: ["#","#","#","Clear","a","cache","value",":","param","key",":","is","the","name","used","when","caching"]
code: def clear(self, key: str):
      """
      ### Clear a cache value
  
      :param key: is the name used when caching
      """
      del self._cache[key]
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/neox/utils/cache.py
imports: from typing import Any
label_desc: Use image node_id 8 for calling the Cache obj's underlying member method code with example usage: obj.clear(key) without return types