code: def _input_fn(
      file_pattern: List[str],
      data_accessor: tfx.components.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 materialized transformed 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,
          tfxio.TensorFlowDatasetOptions(
              batch_size=batch_size, label_key=_LABEL_KEY
          ),
          tf_transform_output.transformed_metadata.schema,
      )
      dataset = dataset.repeat()
      return dataset.prefetch(tf.data.AUTOTUNE)
  
file_path: tfx/tfx/examples/bert/cola/bert_cola_utils.py
imports: from typing import List
  import tensorflow
  import tensorflow_data_validation
  import tensorflow_hub
  import tensorflow_transform
  from tfx import v1
  from tfx.components.transform import stats_options_util
  from tfx.examples.bert.utils.bert_models import build_and_compile_bert_classifier
  from tfx.examples.bert.utils.bert_tokenizer_utils import BertPreprocessor
  from tfx_bsl.public import tfxio
  from google.protobuf import text_format
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