code: def __init__(
      self,
      metric_alone_epochs=0,
      g_alone_epochs=0,
      g_triplets_per_anchor=100,
      *args,
      **kwargs,
  ):
      super().__init__(*args, **kwargs)
      self.original_loss_weights = copy.deepcopy(self.loss_weights)
      self.metric_alone_epochs = metric_alone_epochs
      self.g_alone_epochs = g_alone_epochs
      assert isinstance(
          self.loss_funcs["g_adv_loss"], TripletMarginLoss
      )
      self.loss_funcs["g_adv_loss"].margin *= -1
      self.loss_funcs["g_adv_loss"].distance = NegativeLpDistance()
      self.g_triplets_per_anchor = g_triplets_per_anchor
  
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 1 to create a new DeepAdversarialMetricLearning object from inherited base classes: TrainWithClassifier with example: obj = DeepAdversarialMetricLearning(metric_alone_epochs, g_alone_epochs, g_triplets_per_anchor)