code: def _input_fn(
      file_pattern: List[str],
      data_accessor: DataAccessor,
      tf_transform_output: tft.TFTransformOutput,
      batch_size: int,
  ) -> 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.
      """
      return data_accessor.tf_dataset_factory(
          file_pattern,
          dataset_options.TensorFlowDatasetOptions(
              batch_size=batch_size, label_key=_LABEL_KEY
          ),
          tf_transform_output.transformed_metadata.schema,
      ).repeat()
  
imports: from typing import List
  import absl
  from keras.callbacks import LambdaCallback
  import tensorflow
  import tensorflow_transform
  from tfx.components.trainer.executor import TrainerFnArgs
  from tfx.components.trainer.fn_args_utils import DataAccessor
  from tfx_bsl.tfxio import dataset_options
label_desc: Use image node_id 10 for calling a global function with example usage: _input_fn(file_pattern, data_accessor, tf_transform_output, batch_size) and returns: data_accessor