class_docstr: Pairwise ranking model
  This model takes in a query, and two responses (pos_response and neg_response)
  It passes representations of the query and the two responses to a decoder
  pos_response should be ranked higher than neg_response - this is ensured by training
  with a ranking hinge loss function
code: def arrange_targets(self, tensor_dict):
      return {}
  
file_path: pytext/pytext/models/query_document_pairwise_ranking_model.py
imports: from typing import Dict, List, Tuple
  import torch
  import torch.nn
  from pytext.data.tensorizers import Tensorizer, TokenTensorizer, VocabBuilder
  from pytext.models.decoders.mlp_decoder_query_response import MLPDecoderQueryResponse
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from pytext.models.output_layers import PairwiseRankingOutputLayer
  from pytext.models.pair_classification_model import PairwiseModel
label_desc: Use image node_id 3 for calling the QueryDocPairwiseRankingModel obj's underlying member method code with example usage: obj.arrange_targets(tensor_dict) and returns: dict