class_docstr: Trains, evaluates, and saves a Keras model.
code: def __init__(
      self,
      model: tf.keras.Model,
      train_dataset: tf.data.Dataset,
      eval_dataset: tf.data.Dataset,
      storage: Storage,
      tensorboard_base_dir: str = "/tmp",
  ):
      self._model = model
      self._train_dataset = train_dataset
      self._eval_dataset = eval_dataset
      self._storage = storage
      self._tensorboard_base_dir = tensorboard_base_dir
  
file_path: adanet/adanet/experimental/work_units/keras_trainer_work_unit.py
imports: import os
  import time
  from adanet.experimental.storages.storage import ModelContainer
  from adanet.experimental.storages.storage import Storage
  from adanet.experimental.work_units import work_unit
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new KerasTrainerWorkUnit object from inherited base classes: work_unit with example: obj = KerasTrainerWorkUnit(model, train_dataset, eval_dataset, storage, tensorboard_base_dir)