code: def _make_training_input_fn(
      tf_transform_output, transformed_examples, batch_size
  ):
      """Creates an input function reading from transformed data.
  
      Args:
        tf_transform_output: Wrapper around output of tf.Transform.
        transformed_examples: Base filename of examples.
        batch_size: Batch size.
  
      Returns:
        The input function for training or eval.
      """
  
      def input_fn():
          """Input function for training and eval."""
          dataset = tf.data.experimental.make_batched_features_dataset(
              file_pattern=transformed_examples,
              batch_size=batch_size,
              features=tf_transform_output.transformed_feature_spec(),
              reader=tf.data.TFRecordDataset,
              shuffle=True,
          )
  
          transformed_features = _make_inputs_dense(
              tf.compat.v1.data.make_one_shot_iterator(
                  dataset
              ).get_next()
          )
  
          # Extract features and label from the transformed tensors.
          # TODO(b/30367437): make transformed_labels a dict.
          transformed_labels = tf.where(
              tf.equal(transformed_features.pop(common.LABEL_KEY), 1)
          )
  
          return transformed_features, transformed_labels[:, 1]
  
      return input_fn
  
file_path: transform/examples/census_example.py
imports: import os
  import pprint
  import tempfile
  import tensorflow
  from tensorflow import estimator
  import tensorflow_transform
  import census_example_common
label_desc: Use image node_id 2 for calling a global function with example usage: _make_training_input_fn(tf_transform_output, transformed_examples, batch_size) and returns: input_fn, transformed_features,  transformed_labels