class_docstr: An abstract ensembler.
code: def __init__(self, loss, optimizer, metrics):
      self._loss = loss
      self._optimizer = optimizer
      self._metrics = metrics
  
imports: import abc
  import random
  from typing import Iterable, Iterator, List
  from adanet.experimental.keras.ensemble_model import EnsembleModel
  from adanet.experimental.keras.ensemble_model import MeanEnsemble
  from adanet.experimental.phases.phase import DatasetProvider
  from adanet.experimental.phases.phase import ModelProvider
  from adanet.experimental.storages.in_memory_storage import InMemoryStorage
  from adanet.experimental.storages.storage import Storage
  from adanet.experimental.work_units.keras_trainer_work_unit import KerasTrainerWorkUnit
  from adanet.experimental.work_units.work_unit import WorkUnit
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new Ensembler object from inherited base classes: abc with example: obj = Ensembler(loss, optimizer, metrics)