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: Ensure `defaultdict` can be pickled.
docstr_tok: ["Ensure","`","defaultdict","`","can","be","pickled","."]
code: def _get_tokens_idf_default_value(self) -> float:
      """Ensure `defaultdict` can be pickled."""
      return math.log((self.num_sentences + 1) / 1)
  
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 5 for calling the TextDataset obj's underlying member method code with example usage: obj._get_tokens_idf_default_value() and returns: math