code: def step_optimizers(self):
      step_list = []
      if self.do_metric:
          step_list += [
              "trunk_optimizer",
              "embedder_optimizer",
              "classifier_optimizer",
          ]
      if self.do_adv:
          step_list += ["generator_optimizer"]
      for k in self.optimizers.keys():
          if k in step_list:
              self.optimizers[k].step()
  
imports: import copy
  import torch
  from ..distances import LpDistance
  from ..losses import TripletMarginLoss
  from ..miners import EmbeddingsAlreadyPackagedAsTriplets
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .train_with_classifier import TrainWithClassifier
label_desc: Use image node_id 6 for calling the DeepAdversarialMetricLearning obj's underlying member method code with example usage: obj.step_optimizers() without return types