class_docstr: In memory storage for testing-only.
  
  Uses a priority queue under the hood to sort the models according to their
  score.
  
  Currently the only supported score is 'loss'.
code: def __init__(self):
      self._model_containers = []
  
file_path: adanet/adanet/experimental/storages/in_memory_storage.py
imports: import heapq
  from typing import List
  from adanet.experimental.storages.storage import ModelContainer
  from adanet.experimental.storages.storage import Storage
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new InMemoryStorage object from inherited base classes: Storage with example: obj = InMemoryStorage()