code: def _input_fn(
      file_pattern: List[str],
      data_accessor: tfx.components.DataAccessor,
      schema: schema_pb2.Schema,
      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.
        schema: Schema from TFTransform component.
        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=_transformed_name(_LABEL_KEY),
          ),
          schema,
      ).repeat()
  
imports: import datetime
  import os
  from typing import List
  from absl import logging
  import keras_tuner
  import tensorflow
  from tensorflow import keras
  import tensorflow_transform
  import tfx.v1
  from tfx_bsl.public import tfxio
  from tensorflow_cloud.core import machine_config
  from tensorflow_cloud.tuner import tuner
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 4 for calling a global function with example usage: _input_fn(file_pattern, data_accessor, schema, batch_size) and returns: data_accessor