code: def _apply_feature_constraints(feature, min_value, max_value):
      """Constrains `feature` to be between `min_value` and `max_value`."""
      if min_value is not None:
          feature = tf.math.maximum(feature, min_value)
      if max_value is not None:
          feature = tf.math.minimum(feature, max_value)
      return feature
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from absl import logging
  from neural_structured_learning.lib import abstract_gen_neighbor
  from neural_structured_learning.lib import utils
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: _apply_feature_constraints(feature, min_value, max_value) and returns: feature