code: def get_ordered_indexes(self, split: str) -> torch.LongTensor:
      if split == "val":
          ids = self.corpus.population.data_split().val
      elif split == "test":
          ids = self.corpus.population.data_split().test
      else:
          raise ValueError()
  
      population = self.corpus.population.population()
      targets = population.loc[ids]["TARGET"].values
      pos_idx = np.where(targets == 1)[0]
      neg_idx = np.where(targets == 0)[0]
      np.random.seed(0)
      np.random.shuffle(neg_idx)
      return torch.LongTensor(np.hstack([pos_idx, neg_idx]))
  
imports: import enum
  import logging
  import pickle
  from dataclasses import dataclass, replace
  from pathlib import Path
  from typing import Any, Dict, List, Optional
  from typing_extensions import dataclass_transform
  import dask
  import dask.dataframe
  import pandas
  import numpy
  import pytorch_lightning
  import torch
  from pandas.tseries.offsets import MonthEnd
  from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler
  from ..tasks.base import Task, collate_encoded_documents
  from .sampler import FixedSampler
  from .dataset import DocumentDataset, ShardedDocumentDataset
  from .decorators import save_parquet, save_pickle
  from .ops import concat_columns_dask, concat_sorted
  from .populations.base import Population
  from .serialize import DATA_ROOT, ValidationError, _jsonify
  from .sources.base import Field, TokenSource
  from .vocabulary import Vocabulary
label_desc: Use image node_id 2 for calling the CLSDataModule obj's underlying member method code with example usage: obj.get_ordered_indexes(split) and returns: torch