code: def get_loss(
      self,
      logits: Tuple[torch.Tensor, torch.Tensor],
      targets: torch.Tensor,
      context: Optional[Dict[str, Any]] = None,
      reduce: bool = True,
  ) -> torch.Tensor:
      def _transform_logits(logits):
          if isinstance(self.loss_fn, CosineEmbeddingLoss):
              return logits
          elif isinstance(self.loss_fn, NLLLoss):
              # log probability shall be returned in this case
              # the factor 2.3 = log(10) is used to make sure the consistency between matric reporting and loss function
              cosine_sim_scores = F.cosine_similarity(
                  logits[0], logits[1], dim=1
              )
              pos_scores = cosine_sim_scores * 2.0 * 2.3
              neg_scores = (1.0 - cosine_sim_scores) * 2.0 * 2.3
  
              return F.log_softmax(
                  torch.stack((neg_scores, pos_scores), dim=1), dim=1
              )
          else:
              return F.cosine_similarity(logits[0], logits[1], dim=1)
  
      def _transform_targets(targets):
          if isinstance(
              self.loss_fn, (BinaryCrossEntropyLoss, NLLLoss)
          ):
              return targets
          if isinstance(self.loss_fn, (MAELoss, MSELoss)):
              return targets.to(dtype=torch.float)
          # Replace label = 0 with -1 because we're using cosine_embedding_loss.
          targets = targets.masked_fill(mask=(targets == 0), value=-1.0)
          return targets.to(dtype=torch.float)
  
      logits = _transform_logits(logits)
      targets = _transform_targets(targets)
      return super().get_loss(logits, targets, context, reduce)
  
imports: from enum import IntEnum, unique
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from pytext.config.component import create_loss
  from pytext.data.utils import Vocabulary
  from pytext.fields import FieldMeta
  from pytext.loss import BinaryCrossEntropyLoss, CosineEmbeddingLoss, MAELoss, MSELoss, NLLLoss
  from pytext.models.output_layers.output_layer_base import OutputLayerBase
  from pytext.utils.label import get_label_weights
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 3 for calling the PairwiseCosineDistanceOutputLayer obj's underlying member method code with example usage: obj.get_loss(logits, targets, context, reduce) and returns: super, targets, logits, targets, targets, F, F