docstr: Create tensorflow ops for clipping the gradients according to the given GradientClippingMethod
  :return: None
code: def _create_gradient_clipping_ops(self):
      """
      Create tensorflow ops for clipping the gradients according to the given GradientClippingMethod
      :return: None
      """
      if (
          self.network_parameters.gradients_clipping_method
          == GradientClippingMethod.ClipByGlobalNorm
      ):
          self.clipped_grads, self.grad_norms = tf.clip_by_global_norm(
              self.tensor_gradients,
              self.network_parameters.clip_gradients,
          )
      elif (
          self.network_parameters.gradients_clipping_method
          == GradientClippingMethod.ClipByValue
      ):
          self.clipped_grads = [
              tf.clip_by_value(
                  grad,
                  -self.network_parameters.clip_gradients,
                  self.network_parameters.clip_gradients,
              )
              for grad in self.tensor_gradients
          ]
      elif (
          self.network_parameters.gradients_clipping_method
          == GradientClippingMethod.ClipByNorm
      ):
          self.clipped_grads = [
              tf.clip_by_norm(
                  grad, self.network_parameters.clip_gradients
              )
              for grad in self.tensor_gradients
          ]
  
imports: import os
  import time
  from typing import Any, List, Tuple, Dict
  import numpy
  import tensorflow
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.architectures.tensorflow_components.savers import GlobalVariableSaver
  from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import GradientClippingMethod
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list, squeeze_list, start_shell_command_and_wait
label_desc: Use image node_id 7 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj._create_gradient_clipping_ops() without return types