code: def apply_feature_mask(features, feature_mask=None):
      """Applies a feature mask on `features` if the `feature_mask` is not `None`.
  
      Args:
        features: A dense tensor representing features.
        feature_mask: A dense tensor with values in `[0, 1]` and a broadcastable
          shape to `features`. If not set, or set to `None`, the `features` are
          returned unchanged.
  
      Returns:
        A dense tensor having the same shape as `features`.
      """
      if feature_mask is None:
          return features
      # feature_mask values need to be in [0, 1].
      with tf.control_dependencies(
          [
              tf.debugging.assert_greater_equal(feature_mask, 0.0),
              tf.debugging.assert_less_equal(feature_mask, 1.0),
          ]
      ):
          return features * tf.cast(feature_mask, features.dtype)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import neural_structured_learning.configs
  import six
  import tensorflow
label_desc: Use image node_id 12 for calling a global function with example usage: apply_feature_mask(features, feature_mask) and returns: features, unknown