class_docstr: Optimizer that implements the LAMBOptimizer as tf.train.Optimizer.
docstr: Apply gradients to variables.
docstr_tok: ["Apply","gradients","to","variables","."]
code: def apply_gradients(
      self, grads_and_vars, global_step=None, name=None
  ):
      """Apply gradients to variables."""
      if not self.clip_by_global_norm_after_gradient_allreduce:
          return super(LAMBOptimizer, self).apply_gradients(
              grads_and_vars, global_step, name
          )
  
      tf.logging.info("clip_by_global_norm within LAMB optimizer.")
  
      grads = []
      vars_ = []
      for g, v in grads_and_vars:
          grads.append(g)
          vars_.append(v)
  
      (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)
  
      return super(LAMBOptimizer, self).apply_gradients(
          list(zip(grads, vars_)), global_step, name
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import re
  import tensorflow.compat.v1
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import state_ops
  from tensorflow.python.training import optimizer
label_desc: Use image node_id 5 for calling the LAMBOptimizer obj's underlying member method code with example usage: obj.apply_gradients(grads_and_vars, global_step, name) and returns: super, super