code: def clip_gradient_norms(gradients_to_variables, max_norm):
      """Clips the gradients by the given value.
  
      Args:
        gradients_to_variables: A list of gradient to variable pairs (tuples).
        max_norm: the maximum norm value.
  
      Returns:
        A list of clipped gradient to variable pairs.
      """
      clipped_grads_and_vars = []
      for grad, var in gradients_to_variables:
          if grad is not None:
              if isinstance(grad, tf.IndexedSlices):
                  tmp = tf.clip_by_norm(grad.values, max_norm)
                  grad = tf.IndexedSlices(
                      tmp, grad.indices, grad.dense_shape
                  )
              else:
                  grad = tf.clip_by_norm(grad, max_norm)
          clipped_grads_and_vars.append((grad, var))
      return clipped_grads_and_vars
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import functools
  import inspect
  from absl import logging
  import numpy
  import six
  import tensorflow
  from tf_agents.utils import common
  from tensorflow.python.util import tf_decorator
label_desc: Use image node_id 6 for calling a global function with example usage: clip_gradient_norms(gradients_to_variables, max_norm) and returns: clipped_grads_and_vars