code: def get_logits(self, embeddings):
      sim_to_centers = self.distance(embeddings, self.fc.t())
      sim_to_centers = sim_to_centers.view(
          -1, self.num_classes, self.centers_per_class
      )
      prob = F.softmax(sim_to_centers * self.gamma, dim=2)
      return torch.sum(prob * sim_to_centers, dim=2)
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/soft_triple_loss.py
imports: import math
  import torch
  import torch.nn.functional
  from ..distances import CosineSimilarity
  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 6 for calling the SoftTripleLoss obj's underlying member method code with example usage: obj.get_logits(embeddings) and returns: torch