class_docstr: PyTorch dataset class for storing tokenized sentences and other properties used for BERT score calculation.
class_docstr_tok: ["PyTorch","dataset","class","for","storing","tokenized","sentences","and","other","properties","used","for","BERT","score","calculation","."]
docstr: Return set of tokens from the `input_ids` :class:`~torch.Tensor`.
docstr_tok: ["Return","set","of","tokens","from","the","`","input_ids","`",":","class",":","`","~torch.Tensor","`","."]
code: def _set_of_tokens(input_ids: Tensor) -> Set:
      """Return set of tokens from the `input_ids` :class:`~torch.Tensor`."""
      return set(input_ids.tolist())
  
imports: import math
  import os
  from collections import Counter, defaultdict
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, Union
  import torch
  from torch import Tensor
  from torch.utils.data import DataLoader, Dataset
  from torchmetrics.utilities.data import _cumsum
  from torchmetrics.utilities.imports import _TQDM_AVAILABLE, _TRANSFORMERS_GREATER_EQUAL_4_4
label_desc: Use image node_id 6 for calling the TextDataset obj's underlying member method code with example usage: obj._set_of_tokens(input_ids) and returns: set