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: ### Retrieve a value from cache
  
  :param key: is the name used when caching
  :param default: is the default value if the cache is empty
  :return: the cached value
docstr_tok: ["#","#","#","Retrieve","a","value","from","cache",":","param","key",":","is","the","name","used","when","caching",":","param","default",":","is","the","default","value","if","the","cache","is","empty",":","return",":","the","cached","value"]
code: def get(self, key: str, default: Any = None):
      """
      ### Retrieve a value from cache
  
      :param key: is the name used when caching
      :param default: is the default value if the cache is empty
      :return: the cached value
      """
      return self._cache.get(key, default)
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/neox/utils/cache.py
imports: from typing import Any
label_desc: Use image node_id 7 for calling the Cache obj's underlying member method code with example usage: obj.get(key, default) and returns: self