code: def get_pred(
      self, logits: torch.Tensor, targets: torch.Tensor, *args, **kwargs
  ):
      cosine_sim_scores = F.cosine_similarity(
          logits[0], logits[1], dim=1
      )
      if self.score_type == OutputScore.raw_cosine:
          preds = (cosine_sim_scores >= self.score_threshold).to(
              dtype=torch.long
          )
  
          # Since metric reporting depends on returning a distribution over labels,
          # we will fake a distribution over two labels. We will insert the distance
          # at pred_index (pred_index = 0 or 1) in the scores tensor.
          scores = torch.zeros(
              logits[0].size(0), 2, device=logits[0].device
          )
          scores[:, 0] = (1.0 - cosine_sim_scores[:]) * 2.0
          scores[:, 1] = cosine_sim_scores[:] * 2.0
      else:
          pos_scores, neg_scores = (
              get_norm_cosine_scores(cosine_sim_scores)
              if self.score_type == OutputScore.norm_cosine
              else get_sigmoid_scores(cosine_sim_scores)
          )
  
          preds = (pos_scores >= self.score_threshold).to(
              dtype=torch.long
          )
          scores = torch.cat(
              (neg_scores.unsqueeze(1), pos_scores.unsqueeze(1)), dim=1
          )
  
      return preds, scores
  
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 4 for calling the PairwiseCosineDistanceOutputLayer obj's underlying member method code with example usage: obj.get_pred(logits, targets) and returns: preds,  scores