class_docstr: This loss was used in our SBERT publication (https://arxiv.org/abs/1908.10084) to train the SentenceTransformer
  model on NLI data. It adds a softmax classifier on top of the output of two transformer networks.
  
  :param model: SentenceTransformer model
  :param sentence_embedding_dimension: Dimension of your sentence embeddings
  :param num_labels: Number of different labels
  :param concatenation_sent_rep: Concatenate vectors u,v for the softmax classifier?
  :param concatenation_sent_difference: Add abs(u-v) for the softmax classifier?
  :param concatenation_sent_multiplication: Add u*v for the softmax classifier?
  :param loss_fct: Optional: Custom pytorch loss function. If not set, uses nn.CrossEntropyLoss()
  
  Example::
  
      from sentence_transformers import SentenceTransformer, SentencesDataset, losses
      from sentence_transformers.readers import InputExample
  
      model = SentenceTransformer('distilbert-base-nli-mean-tokens')
      train_examples = [InputExample(texts=['First pair, sent A', 'First pair, sent B'], label=0),
          InputExample(texts=['Second Pair, sent A', 'Second Pair, sent B'], label=3)]
      train_dataset = SentencesDataset(train_examples, model)
      train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=train_batch_size)
      train_loss = losses.SoftmaxLoss(model=model, sentence_embedding_dimension=model.get_sentence_embedding_dimension(), num_labels=train_num_labels)
code: def forward(
      self,
      sentence_features: Iterable[Dict[str, Tensor]],
      labels: Tensor,
  ):
      reps = [
          self.model(sentence_feature)["sentence_embedding"]
          for sentence_feature in sentence_features
      ]
      rep_a, rep_b = reps
  
      vectors_concat = []
      if self.concatenation_sent_rep:
          vectors_concat.append(rep_a)
          vectors_concat.append(rep_b)
  
      if self.concatenation_sent_difference:
          vectors_concat.append(torch.abs(rep_a - rep_b))
  
      if self.concatenation_sent_multiplication:
          vectors_concat.append(rep_a * rep_b)
  
      features = torch.cat(vectors_concat, 1)
  
      output = self.classifier(features)
  
      if labels is not None:
          loss = self.loss_fct(output, labels.view(-1))
          return loss
      else:
          return reps, output
  
file_path: sentence-transformers/sentence_transformers/losses/SoftmaxLoss.py
imports: import torch
  from torch import nn, Tensor
  from typing import Iterable, Dict, Callable
  from ..SentenceTransformer import SentenceTransformer
  import logging
label_desc: Use image node_id 2 for calling the SoftmaxLoss obj's underlying member method code with example usage: obj.forward(sentence_features, labels) and returns: loss, reps,  output