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'.
docstr: Returns the top `num_models` stored models in descending order.
code: def get_best_models(
      self, num_models: int = 1
  ) -> List[tf.keras.Model]:
      """Returns the top `num_models` stored models in descending order."""
      return [
          c.model
          for c in heapq.nsmallest(num_models, 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 4 for calling the InMemoryStorage obj's underlying member method code with example usage: obj.get_best_models(num_models) and returns: unknown