code: def calculate_loss(self, curr_batch):
      data, labels = curr_batch
      penultimate_embeddings = self.get_trunk_output(data)
  
      if self.do_metric:
          authentic_final_embeddings = self.get_final_embeddings(
              penultimate_embeddings
          )
          indices_tuple = self.maybe_mine_embeddings(
              authentic_final_embeddings, labels
          )
          self.losses["metric_loss"] = self.loss_funcs["metric_loss"](
              authentic_final_embeddings, labels, indices_tuple
          )
          logits = self.maybe_get_logits(authentic_final_embeddings)
          self.losses[
              "classifier_loss"
          ] = self.maybe_get_classifier_loss(logits, labels)
  
      if self.do_adv:
          self.calculate_synth_loss(penultimate_embeddings, labels)
  
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 3 for calling the DeepAdversarialMetricLearning obj's underlying member method code with example usage: obj.calculate_loss(curr_batch) without return types