docstr: Compute the loss for the given sample.
  
  Returns a tuple with three elements:
  1) the loss
  2) the sample size, which is used as the denominator for the gradient
  3) logging outputs to display while training
code: def forward(self, model, sample, reduce=True):
      """Compute the loss for the given sample.
  
      Returns a tuple with three elements:
      1) the loss
      2) the sample size, which is used as the denominator for the gradient
      3) logging outputs to display while training
      """
      assert (
          hasattr(model, "classification_heads")
          and self.classification_head_name
          in model.classification_heads
      ), "model must provide sentence classification head for --criterion=sentence_prediction"
  
      token_embeddings = model.encoder.sentence_encoder.embed_tokens(
          sample["net_input"]["src_tokens"]
      )
      input_logits, _ = model(
          **sample["net_input"],
          features_only=True,
          classification_head_name=self.classification_head_name,
          token_embeddings=token_embeddings,
      )
      if model.training and self.noise_sampler:
          noise = self.noise_sampler.sample(
              sample_shape=token_embeddings.shape
          ).to(token_embeddings)
          noised_embeddings = token_embeddings.detach().clone() + noise
  
          noised_logits, _ = model(
              **sample["net_input"],
              features_only=True,
              classification_head_name=self.classification_head_name,
              token_embeddings=noised_embeddings,
          )
          symm_kl = self._get_symm_kl(noised_logits, input_logits)
      else:
          symm_kl = 0
  
      targets = model.get_targets(sample, [input_logits]).view(-1)
      sample_size = targets.numel()
  
      if not self.regression_target:
          loss = F.nll_loss(
              F.log_softmax(input_logits, dim=-1, dtype=torch.float32),
              targets,
              reduction="sum",
          )
          if model.training:
              symm_kl = symm_kl * sample_size
              loss = loss + self.r3f_lambda * symm_kl
      else:
          logits = input_logits.squeeze().float()
          targets = targets.float()
          loss = F.mse_loss(logits, targets, reduction="sum")
  
      logging_output = {
          "loss": utils.item(loss.data) if reduce else loss.data,
          "ntokens": sample["ntokens"],
          "nsentences": sample_size,
          "sample_size": sample_size,
      }
  
      if not self.regression_target:
          preds = input_logits.max(dim=1)[1]
          logging_output.update(
              ncorrect=(preds == targets).sum().item()
          )
  
          if model.training and self.noise_sampler:
              logging_output.update(
                  symm_kl=utils.item(symm_kl.data)
                  if reduce
                  else symm_kl.data
              )
      return loss, sample_size, logging_output
  
file_path: fairseq/examples/rxf/rxf_src/sentence_prediction_r3f.py
imports: import math
  import torch
  import torch.nn.functional
  from fairseq import utils
  from fairseq.criterions import FairseqCriterion, register_criterion
label_desc: Use image node_id 4 for calling the SentencePredictionR3F obj's underlying member method code with example usage: obj.forward(model, sample, reduce) and returns: loss,  sample_size,  logging_output