code: def get_feature_columns(tf_transform_output):
      """Returns the FeatureColumns for the model.
  
      Args:
        tf_transform_output: A `TFTransformOutput` object.
  
      Returns:
        A list of FeatureColumns.
      """
      del tf_transform_output  # unused
      # Unrecognized tokens are represented by -1, but
      # categorical_column_with_identity uses the mod operator to map integers
      # to the range [0, bucket_size).  By choosing bucket_size=VOCAB_SIZE + 1, we
      # represent unrecognized tokens as VOCAB_SIZE.
      review_column = (
          tf.feature_column.categorical_column_with_identity(
              REVIEW_KEY, num_buckets=VOCAB_SIZE + 1
          )
      )
      weighted_reviews = tf.feature_column.weighted_categorical_column(
          review_column, REVIEW_WEIGHT_KEY
      )
  
      return [weighted_reviews]
  
file_path: transform/examples/sentiment_example.py
imports: import argparse
  import os
  import pprint
  import tempfile
  import apache_beam
  import tensorflow
  from tensorflow import estimator
  import tensorflow_transform
  import tensorflow_transform.beam
  from tfx_bsl.public import tfxio
label_desc: Use image node_id 7 for calling a global function with example usage: get_feature_columns(tf_transform_output) and returns: list