code: def calculate_synth_loss(self, penultimate_embeddings, labels):
      a_indices, p_indices, n_indices = lmu.convert_to_triplets(
          None, labels, t_per_anchor=self.g_triplets_per_anchor
      )
      real_anchors = penultimate_embeddings[a_indices]
      real_positives = penultimate_embeddings[p_indices]
      real_negatives = penultimate_embeddings[n_indices]
      penultimate_embeddings_cat = torch.cat(
          [real_anchors, real_positives, real_negatives], dim=1
      )
      synthetic_negatives = self.models["generator"](
          c_f.to_device(
              penultimate_embeddings_cat, device=self.data_device
          )
      )
      penultimate_embeddings_with_negative_synth = c_f.unslice_by_n(
          [real_anchors, real_positives, synthetic_negatives]
      )
      final_embeddings = self.get_final_embeddings(
          penultimate_embeddings_with_negative_synth
      )
  
      labels = torch.tensor(
          [
              val
              for tup in zip(
                  *[
                      labels[a_indices],
                      labels[p_indices],
                      labels[n_indices],
                  ]
              )
              for val in tup
          ]
      )
  
      indices_tuple = self.mining_funcs["synth_packaged_as_triplets"](
          final_embeddings, labels
      )
  
      if self.do_both:
          self.losses["synth_loss"] = self.loss_funcs["synth_loss"](
              final_embeddings, labels, indices_tuple
          )
  
      self.losses["g_adv_loss"] = self.loss_funcs["g_adv_loss"](
          final_embeddings, labels, indices_tuple
      )
      self.losses["g_hard_loss"] = torch.nn.functional.mse_loss(
          torch.nn.functional.normalize(
              synthetic_negatives, p=2, dim=1
          ),
          torch.nn.functional.normalize(real_anchors, p=2, dim=1),
      )
      self.losses["g_reg_loss"] = torch.nn.functional.mse_loss(
          torch.nn.functional.normalize(
              synthetic_negatives, p=2, dim=1
          ),
          torch.nn.functional.normalize(real_negatives, p=2, dim=1),
      )
  
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 7 for calling the DeepAdversarialMetricLearning obj's underlying member method code with example usage: obj.calculate_synth_loss(penultimate_embeddings, labels) without return types