class_docstr: This is a classification model that selects the best heteronym option out of possible dictionary entries.
  Supports only heteronyms, no OOV.
code: def _setup_infer_dataloader(
      self,
      manifest: str,
      grapheme_field: str,
      batch_size: int,
      num_workers: int,
  ) -> "torch.utils.data.DataLoader":
      dataset = HeteronymClassificationDataset(
          manifest=manifest,
          grapheme_field=grapheme_field,
          tokenizer=self.tokenizer,
          wordid_to_idx=self.wordid_to_idx,
          heteronym_dict=self.heteronym_dict,
          max_seq_len=self.tokenizer.tokenizer.model_max_length,
          with_labels=False,
      )
  
      return torch.utils.data.DataLoader(
          dataset,
          collate_fn=dataset.collate_fn,
          batch_size=batch_size,
          shuffle=False,
          num_workers=num_workers,
          drop_last=False,
      )
  
imports: import json
  import os
  from typing import List, Optional
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.common.losses import CrossEntropyLoss
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.modules.common import TokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.collections.tts.g2p.data.heteronym_classification import HeteronymClassificationDataset
  from nemo.collections.tts.g2p.utils import get_heteronym_spans, get_wordid_to_phonemes, read_wordids
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 19 for calling the HeteronymClassificationModel obj's underlying member method code with example usage: obj._setup_infer_dataloader(manifest, grapheme_field, batch_size, num_workers) and returns: torch