code: def _get_dataloader(
      input_ids: Tensor,
      attention_mask: Tensor,
      idf: bool,
      batch_size: int,
      num_workers: int,
  ) -> DataLoader:
      """Prepare dataloader.
  
      Args:
          input_ids:
              Indices of input sequence tokens in the vocabulary.
          attention_mask:
              Mask to avoid performing attention on padding token indices.
          idf:
              A bool indicating whether normalization using inverse document frequencies should be used.
          batch_size:
              A batch size used for model processing.
          num_workers:
              A number of workers to use for a dataloader.
  
      Return:
          An instance of ``torch.utils.data.DataLoader`` used for iterating over examples.
  
      """
      dataset = TokenizedDataset(input_ids, attention_mask, idf)
      return DataLoader(
          dataset, batch_size=batch_size, num_workers=num_workers
      )
  
imports: import os
  from enum import unique
  from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from typing_extensions import Literal
  from torchmetrics.functional.text.helper_embedding_metric import TokenizedDataset, _get_progress_bar, _input_data_collator, _load_tokenizer_and_model
  from torchmetrics.utilities.enums import EnumStr
  from torchmetrics.utilities.imports import _TRANSFORMERS_GREATER_EQUAL_4_4
label_desc: Use image node_id 1 for calling a global function with example usage: _get_dataloader(input_ids, attention_mask, idf, batch_size, num_workers) and returns: DataLoader