code: def _clip_by_norm(self, values, axes=None):
      # Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
      l2sum = ops.sum(ops.square(values), axes, keepdims=True)
      pred = l2sum > 0
      # Two-tap tf.where trick to bypass NaN gradients
      l2sum_safe = ops.where(pred, l2sum, ops.ones_like(l2sum))
      l2norm = ops.where(pred, ops.sqrt(l2sum_safe), l2sum)
      intermediate = ops.multiply(values, self.clipnorm)
      values_clip = ops.convert_to_tensor(intermediate) / ops.maximum(
          l2norm, self.clipnorm
      )
      return values_clip
  
imports: import re
  import warnings
  import numpy
  from keras import backend
  from keras import initializers
  from keras import ops
  from keras.optimizers.schedules import learning_rate_schedule
  from keras.saving import serialization_lib
  from keras.utils import tracking
  from keras.utils.naming import auto_name
label_desc: Use image node_id 40 for calling the BaseOptimizer obj's underlying member method code with example usage: obj._clip_by_norm(values, axes) and returns: values_clip