code: def get_logits(self, embeddings):
      normalized_W = self.distance.normalize(self.W, dim=0)
      logits = (
          self.distance(embeddings, normalized_W.t()) / self.temperature
      )
      if not self.distance.is_inverted:
          logits = -logits
      return logits
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/normalized_softmax_loss.py
imports: import torch
  from ..distances import DotProductSimilarity
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
  from .mixins import WeightRegularizerMixin
label_desc: Use image node_id 5 for calling the NormalizedSoftmaxLoss obj's underlying member method code with example usage: obj.get_logits(embeddings) and returns: logits