code: def stateless_apply(
      self, optimizer_variables, grads, trainable_variables
  ):
      self._check_super_called()
  
      if not self.built:
          raise ValueError(
              f"To call `stateless_apply`, {self.__class__.__name__} "
              "must be built (i.e. its variables must have been created). "
              "You can build it via `optimizer.build(trainable_variables)`."
          )
      if len(optimizer_variables) != len(self.variables):
          raise ValueError(
              "Argument `optimizer_variables` must be a list of tensors "
              f"corresponding 1:1 to {self.__class__.__name__}().variables. "
              f"Received list with length {len(optimizer_variables)}, but "
              f"expected {len(self.variables)} variables."
          )
      if len(trainable_variables) != len(self._trainable_variables):
          raise ValueError(
              "Argument `optimizer_variables` must be a list of tensors "
              "corresponding 1:1 to the trainable variables list that "
              "the optimizer was built with. Received "
              f"len(trainable_variables) == {len(trainable_variables)} "
              "whereas the optimizer was built with "
              f"{len(self._trainable_variables)} variables."
          )
  
      # Gather variable mapping
      mapping = list(
          zip(self._trainable_variables, trainable_variables)
      ) + list(zip(self.variables, optimizer_variables))
  
      # Call in stateless scope
      with backend.StatelessScope(state_mapping=mapping) as scope:
          self.apply(grads)
  
      # Gather updated variables
      trainable_variables = []
      for v in self._trainable_variables:
          new_v = scope.get_current_value(v)
          if new_v is not None:
              trainable_variables.append(new_v)
          else:
              trainable_variables.append(v)
      optimizer_variables = []
      for v in self.variables:
          new_v = scope.get_current_value(v)
          if new_v is not None:
              optimizer_variables.append(new_v)
          else:
              optimizer_variables.append(v)
      return trainable_variables, optimizer_variables
  
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 20 for calling the BaseOptimizer obj's underlying member method code with example usage: obj.stateless_apply(optimizer_variables, grads, trainable_variables) and returns: trainable_variables,  optimizer_variables