code: def _input_fn(
      file_pattern, data_accessor, tf_transform_output, batch_size=200
  ):
      """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.
      """
      return data_accessor.tf_dataset_factory(
          file_pattern,
          tfxio.TensorFlowDatasetOptions(
              batch_size=batch_size,
              label_key=features.transformed_name(features.LABEL_KEY),
          ),
          tf_transform_output.transformed_metadata.schema,
      ).repeat()
  
file_path: tfx/tfx/experimental/templates/taxi/models/keras_model/model.py
imports: from absl import logging
  import tensorflow
  import tensorflow_transform
  from tfx.experimental.templates.taxi.models import features
  from tfx.experimental.templates.taxi.models.keras_model import constants
  from tfx_bsl.public import tfxio
label_desc: Use image node_id 3 for calling a global function with example usage: _input_fn(file_pattern, data_accessor, tf_transform_output, batch_size) and returns: data_accessor