code: def _load_baseline(
      lang: str = "en",
      model_name_or_path: Optional[str] = None,
      baseline_path: Optional[str] = None,
      baseline_url: Optional[str] = None,
  ) -> Optional[Tensor]:
      """Load a CSV file with the baseline values used for rescaling."""
      if baseline_path:
          baseline: Optional[Tensor] = _read_csv_from_local_file(
              baseline_path
          )
      elif baseline_url:
          baseline = _read_csv_from_url(baseline_url)
      # Read default baseline from the original `bert-score` package https://github.com/Tiiiger/bert_score
      elif lang and model_name_or_path:
          url_base = "https://raw.githubusercontent.com/Tiiiger/bert_score/master/bert_score/rescale_baseline"
          baseline_url = f"{url_base}/{lang}/{model_name_or_path}.tsv"
          baseline = _read_csv_from_url(baseline_url)
      else:
          rank_zero_warn(
              "Baseline was not successfully loaded. No baseline is going to be used."
          )
          return None
  
      return baseline
  
file_path: torchmetrics/src/torchmetrics/functional/text/bert.py
imports: import csv
  import urllib
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.data import DataLoader
  from torchmetrics.functional.text.helper_embedding_metric import TextDataset, TokenizedDataset, _check_shape_of_model_output, _get_progress_bar, _input_data_collator, _output_data_collator, _process_attention_mask_for_special_tokens
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.checks import _SKIP_SLOW_DOCTEST, _try_proceed_with_timeout
  from torchmetrics.utilities.imports import _TQDM_AVAILABLE, _TRANSFORMERS_GREATER_EQUAL_4_4
label_desc: Use image node_id 7 for calling a global function with example usage: _load_baseline(lang, model_name_or_path, baseline_path, baseline_url) and returns: baseline, None