code: def _input_fn(
      file_pattern: List[str],
      data_accessor: DataAccessor,
      tf_transform_output: tft.TFTransformOutput,
      batch_size: int = 200,
  ) -> tf.data.Dataset:
      """Generates features and label for tuning/training.
  
      Args:
        file_pattern: List of paths or patterns of input tfrecord files.
        data_accessor: DataAccessor for converting input to RecordBatch.
        tf_transform_output: A TFTransformOutput.
        batch_size: representing the number of consecutive elements of returned
          dataset to combine in a single batch.
  
      Returns:
        A dataset that contains (features, indices) tuple where features is a
          dictionary of Tensors, and indices is a single Tensor of label indices.
      """
      dataset = data_accessor.tf_dataset_factory(
          file_pattern,
          dataset_options.TensorFlowDatasetOptions(
              batch_size=batch_size,
              label_key=_transformed_name(_LABEL_KEY),
          ),
          tf_transform_output.transformed_metadata.schema,
      )
  
      return dataset.repeat()
  
file_path: tfx/tfx/examples/imdb/imdb_utils_native_keras.py
imports: from typing import List
  import absl
  import tensorflow
  from tensorflow import keras
  import tensorflow_transform
  from tfx.components.trainer.fn_args_utils import DataAccessor
  from tfx.components.trainer.fn_args_utils import FnArgs
  from tfx_bsl.tfxio import dataset_options
label_desc: Use image node_id 4 for calling a global function with example usage: _input_fn(file_pattern, data_accessor, tf_transform_output, batch_size) and returns: dataset