code: def _get_log_probs(self, logits):
      # question_logits: (bsz X rep_dim); context_logits: (bsz * 2, rep_dim)
      question_logits, context_logits = logits
      dot_products = torch.matmul(
          question_logits, torch.transpose(context_logits, 0, 1)
      )  # (bsz X bsz*num_negs+1)
      return F.log_softmax(dot_products, dim=1)
  
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 3 for calling the DenseRetrievalOutputLayer obj's underlying member method code with example usage: obj._get_log_probs(logits) and returns: F