class_docstr: Trains, evaluates and saves a tuned Keras model.
code: def execute(self):
      log_dir = os.path.join("/tmp", str(int(time.time())))
      tensorboard = tf.keras.callbacks.TensorBoard(
          log_dir=log_dir, update_freq="batch"
      )
      # We don't need to eval and store, because the Tuner does it for us.
      self._tuner.search(
          callbacks=[tensorboard],
          *self._search_args,
          **self._search_kwargs,
      )
  
file_path: adanet/adanet/experimental/work_units/keras_tuner_work_unit.py
imports: import os
  import time
  from adanet.experimental.work_units import work_unit
  from kerastuner.engine.tuner import Tuner
  import tensorflow.compat.v2
label_desc: Use image node_id 2 for calling the KerasTunerWorkUnit obj's underlying member method code with example usage: obj.execute() without return types