How to use the ludwig.utils.misc.set_default_value function in ludwig

def add_feature_data(
            feature,
            dataset_df,
            data,
            metadata,
            preprocessing_parameters
    ):
        set_default_value(
            feature['preprocessing'],
            'in_memory',
            preprocessing_parameters['in_memory']
        )
        set_default_value(
            feature['preprocessing'],
            'num_processes',
            preprocessing_parameters['num_processes']
        )
        csv_path = None
        if hasattr(dataset_df, 'csv'):
            csv_path = os.path.dirname(os.path.abspath(dataset_df.csv))

        num_images = len(dataset_df)
        if num_images == 0:
            raise ValueError('There are no images in the dataset provided.')

        first_image_path = dataset_df[feature['name']][0]
        if csv_path is None and not os.path.isabs(first_image_path):
            raise ValueError('Image file paths must be absolute')

def populate_defaults(input_feature):
        set_default_value(input_feature, 'tied_weights', None)

def populate_defaults(input_feature):
        set_default_value(input_feature, 'tied_weights', None)

set_default_value(output_feature[LOSS],
                          'class_similarities_temperature', 0)
        set_default_value(output_feature[LOSS], 'weight', 1)

        if output_feature[LOSS]['type'] == 'sampled_softmax_cross_entropy':
            set_default_value(output_feature[LOSS], 'sampler', 'log_uniform')
            set_default_value(output_feature[LOSS], 'negative_samples', 25)
            set_default_value(output_feature[LOSS], 'distortion', 0.75)
        else:
            set_default_value(output_feature[LOSS], 'sampler', None)
            set_default_value(output_feature[LOSS], 'negative_samples', 0)
            set_default_value(output_feature[LOSS], 'distortion', 1)

        set_default_value(output_feature[LOSS], 'unique', False)

        set_default_value(output_feature, 'decoder', 'generator')

        if output_feature['decoder'] == 'tagger':
            set_default_value(output_feature, 'reduce_input', None)

        set_default_value(output_feature, 'dependencies', [])
        set_default_value(output_feature, 'reduce_input', SUM)
        set_default_value(output_feature, 'reduce_dependencies', SUM)

set_default_value(output_feature[LOSS], 'sampler', 'log_uniform')
            set_default_value(output_feature[LOSS], 'negative_samples', 25)
            set_default_value(output_feature[LOSS], 'distortion', 0.75)
        else:
            set_default_value(output_feature[LOSS], 'sampler', None)
            set_default_value(output_feature[LOSS], 'negative_samples', 0)
            set_default_value(output_feature[LOSS], 'distortion', 1)

        set_default_value(output_feature[LOSS], 'unique', False)

        set_default_value(output_feature, 'decoder', 'generator')

        if output_feature['decoder'] == 'tagger':
            set_default_value(output_feature, 'reduce_input', None)

        set_default_value(output_feature, 'dependencies', [])
        set_default_value(output_feature, 'reduce_input', SUM)
        set_default_value(output_feature, 'reduce_dependencies', SUM)

def populate_defaults(output_feature):
        set_default_value(
            output_feature,
            LOSS,
            {'type': 'mean_absolute_error', 'weight': 1}
        )
        set_default_value(output_feature[LOSS], 'type', 'mean_absolute_error')
        set_default_value(output_feature[LOSS], 'weight', 1)

        set_default_value(output_feature, 'decoder', 'generator')

        if output_feature['decoder'] == 'generator':
            set_default_value(output_feature, 'cell_type', 'rnn')
            set_default_value(output_feature, 'state_size', 256)
            set_default_value(output_feature, 'embedding_size', 1)
            set_default_value(output_feature, 'attention_mechanism', None)
            if output_feature['attention_mechanism'] is not None:
                set_default_value(output_feature, 'reduce_input', None)
            set_default_value(output_feature, 'decoder', 'generator')
            set_default_value(output_feature, 'decoder', 'generator')
            set_default_value(output_feature, 'decoder', 'generator')
            set_default_value(output_feature, 'decoder', 'generator')

        if output_feature['decoder'] == 'tagger':
            if 'reduce_input' not in output_feature:
                output_feature['reduce_input'] = None

        set_default_value(output_feature, 'dependencies', [])
        set_default_value(output_feature, 'reduce_input', SUM)
        set_default_value(output_feature, 'reduce_dependencies', SUM)

def populate_defaults(output_feature):
        set_default_value(
            output_feature,
            LOSS,
            {'type': 'mean_squared_error', 'weight': 1}
        )
        set_default_value(output_feature[LOSS], 'type', 'mean_squared_error')
        set_default_value(output_feature[LOSS], 'weight', 1)

        set_default_values(
            output_feature,
            {
                'clip': None,
                'dependencies': [],
                'reduce_input': SUM,
                'reduce_dependencies': SUM
            }

def populate_defaults(output_feature):

        set_default_value(output_feature, LOSS, {})
        set_default_value(output_feature[LOSS], 'type', MEAN_SQUARED_ERROR)
        set_default_value(output_feature[LOSS], 'weight', 1)
        set_default_value(output_feature, 'reduce_input', None)
        set_default_value(output_feature, 'reduce_dependencies', None)
        set_default_value(output_feature, 'softmax', False)
        set_default_value(output_feature, 'decoder', 'fc_stack')
        set_default_value(output_feature, 'dependencies', [])

)
        set_default_value(output_feature[LOSS], 'type', 'softmax_cross_entropy')
        set_default_value(output_feature[LOSS], 'labels_smoothing', 0)
        set_default_value(output_feature[LOSS], 'class_weights', 1)
        set_default_value(output_feature[LOSS], 'robust_lambda', 0)
        set_default_value(output_feature[LOSS], 'confidence_penalty', 0)
        set_default_value(output_feature[LOSS],
                          'class_similarities_temperature', 0)
        set_default_value(output_feature[LOSS], 'weight', 1)

        if output_feature[LOSS]['type'] == 'sampled_softmax_cross_entropy':
            set_default_value(output_feature[LOSS], 'sampler', 'log_uniform')
            set_default_value(output_feature[LOSS], 'negative_samples', 25)
            set_default_value(output_feature[LOSS], 'distortion', 0.75)
        else:
            set_default_value(output_feature[LOSS], 'sampler', None)
            set_default_value(output_feature[LOSS], 'negative_samples', 0)
            set_default_value(output_feature[LOSS], 'distortion', 1)

        set_default_value(output_feature[LOSS], 'unique', False)

        set_default_value(output_feature, 'decoder', 'generator')

        if output_feature['decoder'] == 'tagger':
            set_default_value(output_feature, 'reduce_input', None)

        set_default_value(output_feature, 'dependencies', [])
        set_default_value(output_feature, 'reduce_input', SUM)
        set_default_value(output_feature, 'reduce_dependencies', SUM)

def populate_defaults(output_feature):
        set_default_value(output_feature, LOSS, {'weight': 1, 'type': None})
        set_default_value(output_feature[LOSS], 'weight', 1)

        set_default_value(output_feature, 'threshold', 0.5)
        set_default_value(output_feature, 'dependencies', [])
        set_default_value(output_feature, 'reduce_input', SUM)
        set_default_value(output_feature, 'reduce_dependencies', SUM)