code: def get_loss(
      self,
      logits: Tuple[torch.Tensor, torch.Tensor],
      targets: torch.Tensor,
      context: Optional[Dict[str, Any]] = None,
      reduce: bool = True,
  ) -> torch.Tensor:
      # Loss computation pointer: https://fburl.com/inf9ra38
      log_probs = self._get_log_probs(logits)
      # only supports NLL loss
      loss = self.loss_fn(log_probs, targets, reduce)
      return loss
  
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 1 for calling the DenseRetrievalOutputLayer obj's underlying member method code with example usage: obj.get_loss(logits, targets, context, reduce) and returns: loss