code: def get_pred(
      self, logits: torch.Tensor, targets: torch.Tensor, *args, **kwargs
  ):
      log_probs = self._get_log_probs(logits)
      _, preds = torch.max(log_probs, 1)
      # Pack in logits and positive_indices_per_question for computing Avg Rank
      question_logits, context_logits = logits
      return (
          (
              preds,
              question_logits.detach().cpu().tolist(),
              context_logits.detach().cpu().tolist(),
          ),
          log_probs,
      )
  
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 2 for calling the DenseRetrievalOutputLayer obj's underlying member method code with example usage: obj.get_pred(logits, targets) and returns: preds,  question_logits,  context_logits,  log_probs