code: def skip_for_tf2(f):
      """Decorator that skips tests when using TensorFlow 2."""
  
      def test_wrapper(*args, **kwargs):
          """Wraps the decorated function to determine whether to skip."""
  
          # Extract test case instance from args.
          self = args[0]
          try:
              # If tf.contrib doesn't exist, we are in TF 2.0.
              _ = tf.contrib
              _ = tf.contrib.estimator.regression_head(
                  loss_reduction=tf.compat.v1.losses.Reduction.SUM_OVER_BATCH_SIZE
              )
          except (AttributeError, ImportError):
              self.skipTest("Skipping test in TF 2.0.")
          return f(*args, **kwargs)
  
      return test_wrapper
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from distutils.version import LooseVersion
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow.python import tf2
  from tensorflow.python.keras.metrics import Metric
  from tensorflow.python.tpu import tpu_function
  from tensorflow_estimator.python.estimator.head import regression_head
label_desc: Use image node_id 8 for calling a global function with example usage: skip_for_tf2(f) and returns: test_wrapper, f