docs: def build_model_columns():
    """Builds a set of wide and deep feature columns."""
    # Continuous variable columns
    age = tf.feature_column.numeric_column('age')
    education_num = tf.feature_column.numeric_column('education_num')
    capital_gain = tf.feature_column.numeric_column('capital_gain')
    capital_loss = tf.feature_column.numeric_column('capital_loss')
    hours_per_week = tf.feature_column.numeric_column('hours_per_week')
  
    education = tf.feature_column.categorical_column_with_vocabulary_list(
        'education', [
            'Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college',
            'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school',
            '5th-6th', '10th', '1st-4th', 'Preschool', '12th'])
  
    marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
        'marital_status', [
            'Married-civ-spouse', 'Divorced', 'Married-spouse-absent',
            'Never-married', 'Separated', 'Married-AF-spouse', 'Widowed'])
  
    relationship = tf.feature_column.categorical_column_with_vocabulary_list(
        'relationship', [
            'Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried',
            'Other-relative'])
  
    workclass = tf.feature_column.categorical_column_with_vocabulary_list(
        'workclass', [
            'Self-emp-not-inc', 'Private', 'State-gov', 'Federal-gov',
            'Local-gov', '?', 'Self-emp-inc', 'Without-pay', 'Never-worked'])
  
    # To show an example of hashing:
    occupation = tf.feature_column.categorical_column_with_hash_bucket(
        'occupation', hash_bucket_size=_HASH_BUCKET_SIZE)
  
    # Transformations.
    age_buckets = tf.feature_column.bucketized_column(
        age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
  
    # Wide columns and deep columns.
    base_columns = [
        education, marital_status, relationship, workclass, occupation,
        age_buckets,
    ]
  
    crossed_columns = [
        tf.feature_column.crossed_column(
            ['education', 'occupation'], hash_bucket_size=_HASH_BUCKET_SIZE),
        tf.feature_column.crossed_column(
            [age_buckets, 'education', 'occupation'],
            hash_bucket_size=_HASH_BUCKET_SIZE),
    ]
  
    wide_columns = base_columns + crossed_columns
  
    deep_columns = [
        age,
        education_num,
        capital_gain,
        capital_loss,
        hours_per_week,
        tf.feature_column.indicator_column(workclass),
        tf.feature_column.indicator_column(education),
        tf.feature_column.indicator_column(marital_status),
        tf.feature_column.indicator_column(relationship),
        # To show an example of embedding
        tf.feature_column.embedding_column(occupation, dimension=8),
    ]
  
    return wide_columns, deep_columns