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.
      """
      feature_spec = tf_transform_output.transformed_feature_spec()
  
      # Wrap scalars as real valued columns.
      def get_shape(spec):
          if isinstance(spec, tf.io.SparseFeature):
              return spec.size
          return spec.shape
  
      return [
          tf.feature_column.numeric_column(
              key, shape=get_shape(feature_spec[key])
          )
          for key in (
              common.NUMERIC_FEATURE_KEYS
              + common.CATEGORICAL_FEATURE_KEYS
          )
      ]
  
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 4 for calling a global function with example usage: get_feature_columns(tf_transform_output) and returns: unknown, spec, spec