class_docstr: Translation rerank task.
  The input can be either (src, tgt) sentence pairs or tgt sentence only.
class_docstr_tok: ["Translation","rerank","task",".","The","input","can","be","either","(","src",",","tgt",")","sentence","pairs","or","tgt","sentence","only","."]
code: def create_dummy_batch(self, device):
      dummy_target = (
          torch.zeros(self.cfg.mt_beam, EVAL_BLEU_ORDER * 2 + 3)
          .long()
          .to(device)
          if not self.cfg.eval_ter
          else torch.zeros(self.cfg.mt_beam, 3).long().to(device)
      )
  
      return {
          "id": torch.zeros(self.cfg.mt_beam, 1).long().to(device),
          "net_input": {
              "src_tokens": torch.zeros(self.cfg.mt_beam, 4)
              .long()
              .to(device),
              "src_lengths": torch.ones(self.cfg.mt_beam, 1)
              .long()
              .to(device),
          },
          "nsentences": 0,
          "ntokens": 0,
          "target": dummy_target,
      }
  
imports: from dataclasses import dataclass, field
  import itertools
  import logging
  import os
  import numpy
  import torch
  from fairseq.logging import metrics
  from fairseq.data import ConcatDataset, ConcatSentencesDataset, data_utils, Dictionary, IdDataset, indexed_dataset, NestedDictionaryDataset, NumSamplesDataset, NumelDataset, PrependTokenDataset, RawLabelDataset, RightPadDataset, SortDataset, TruncateDataset, TokenBlockDataset
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.tasks import FairseqTask, register_task
  from omegaconf import II, MISSING
label_desc: Use image node_id 11 for calling the DiscriminativeRerankingNMTTask obj's underlying member method code with example usage: obj.create_dummy_batch(device) and returns: dict