class_docstr: Creates a TF Privacy-enabled version of MultiLabelHead.
code: def __init__(
      self,
      n_classes,
      weight_column=None,
      thresholds=None,
      label_vocabulary=None,
      loss_reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
      loss_fn=None,
      classes_for_class_based_metrics=None,
      name=None,
  ):
      if loss_reduction == tf.keras.losses.Reduction.NONE:
          loss_reduction = tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE
      super().__init__(
          n_classes=n_classes,
          weight_column=weight_column,
          thresholds=thresholds,
          label_vocabulary=label_vocabulary,
          loss_reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
          loss_fn=loss_fn,
          classes_for_class_based_metrics=classes_for_class_based_metrics,
          name=name,
      )
  
file_path: privacy/tensorflow_privacy/privacy/estimators/multi_label_head.py
imports: import tensorflow
  from tensorflow_estimator.python.estimator import model_fn
  from tensorflow_estimator.python.estimator.canned import prediction_keys
  from tensorflow_estimator.python.estimator.export import export_output
  from tensorflow_estimator.python.estimator.head import base_head
  from tensorflow_estimator.python.estimator.head import multi_label_head
  from tensorflow_estimator.python.estimator.mode_keys import ModeKeys
label_desc: Use image node_id 1 to create a new DPMultiLabelHead object from inherited base classes: multi_label_head with example: obj = DPMultiLabelHead(n_classes, weight_column, thresholds, label_vocabulary, loss_reduction, loss_fn, classes_for_class_based_metrics, name)