code: def _input_fn(
      filenames, data_accessor, tf_transform_output, batch_size=200
  ):
      """Generates features and labels for training or evaluation.
  
      Args:
        filenames: [str] list of CSV files to read data from.
        data_accessor: fn_args_utils.DataAccessor.
        tf_transform_output: A TFTransformOutput.
        batch_size: int First dimension size of the Tensors returned by input_fn
  
      Returns:
        A (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(
          filenames,
          TensorFlowDatasetOptions(
              batch_size=batch_size,
              label_key=_transformed_name(_LABEL_KEY),
          ),
          tf_transform_output.transformed_metadata.schema,
      )
  
      return tf.compat.v1.data.make_one_shot_iterator(
          dataset
      ).get_next()
  
imports: import absl
  import tensorflow
  from tensorflow import estimator
  import tensorflow_model_analysis
  import tensorflow_transform
  from tensorflow_transform.tf_metadata import schema_utils
  from tfx.components.trainer import executor
  from tfx.utils import io_utils
  from tfx.utils import path_utils
  from tfx_bsl.public.tfxio import TensorFlowDatasetOptions
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 8 for calling a global function with example usage: _input_fn(filenames, data_accessor, tf_transform_output, batch_size) and returns: tf