code: def test_training(self):
      def build_model() -> tf.keras.Model:
          return tf.keras.Sequential([tf.keras.layers.Dense(1)])
  
      def train_func():
          strategy = tf.distribute.MultiWorkerMirroredStrategy()
          with strategy.scope():
              multi_worker_model = build_model()
              multi_worker_model.compile(
                  optimizer=tf.keras.optimizers.SGD(),
                  loss=tf.keras.losses.mean_absolute_error,
                  metrics=[tf.keras.metrics.mean_squared_error],
              )
  
          dataset = train.get_dataset_shard("train").to_tf(
              "X", "Y", batch_size=4
          )
          multi_worker_model.fit(dataset)
  
      dataset = ray.data.from_items(8 * [{"X0": 0, "X1": 0, "Y": 0}])
      concatenator = Concatenator(exclude=["Y"], output_column_name="X")
      dataset = concatenator.transform(dataset)
  
      trainer = TensorflowTrainer(
          train_loop_per_worker=train_func,
          scaling_config=ScalingConfig(num_workers=2),
          datasets={"train": dataset},
      )
      trainer.fit()
  
file_path: ray/python/ray/data/tests/test_tf.py
imports: import numpy
  import pandas
  import pytest
  import tensorflow
  import ray
  from ray import train
  from ray.data.preprocessors import Concatenator
  from ray.train import ScalingConfig
  from ray.train.tensorflow import TensorflowTrainer
label_desc: Use image node_id 9 for calling the TestToTF obj's underlying member method code with example usage: obj.test_training() and returns: tf