code: def _bert_score(
      preds: Union[List[str], Dict[str, Tensor]],
      target: Union[List[str], Dict[str, Tensor]],
      model_name_or_path: Optional[str] = None,
      num_layers: Optional[int] = None,
      all_layers: bool = False,
      model: Optional[Module] = None,
      user_tokenizer: Any = None,
      user_forward_fn: Optional[
          Callable[[Module, Dict[str, Tensor]], Tensor]
      ] = None,
      verbose: bool = False,
      idf: bool = False,
      device: Optional[Union[str, torch.device]] = None,
      max_length: int = 512,
      batch_size: int = 64,
      num_threads: int = 4,
      return_hash: bool = False,
      lang: str = "en",
      rescale_with_baseline: bool = False,
      baseline_path: Optional[str] = None,
      baseline_url: Optional[str] = None,
  ) -> Dict[str, Union[Tensor, List[float], str]]:
      """Wrapper for deprecated import.
  
      >>> preds = ["hello there", "general kenobi"]
      >>> target = ["hello there", "master kenobi"]
      >>> score = _bert_score(preds, target)
      >>> from pprint import pprint
      >>> pprint(score)
      {'f1': tensor([1.0000, 0.9961]),
       'precision': tensor([1.0000, 0.9961]),
       'recall': tensor([1.0000, 0.9961])}
  
      """
      _deprecated_root_import_func("bert_score", "text")
      return bert_score(
          preds=preds,
          target=target,
          model_name_or_path=model_name_or_path,
          num_layers=num_layers,
          all_layers=all_layers,
          model=model,
          user_tokenizer=user_tokenizer,
          user_forward_fn=user_forward_fn,
          verbose=verbose,
          idf=idf,
          device=device,
          max_length=max_length,
          batch_size=batch_size,
          num_threads=num_threads,
          return_hash=return_hash,
          lang=lang,
          rescale_with_baseline=rescale_with_baseline,
          baseline_path=baseline_path,
          baseline_url=baseline_url,
      )
  
imports: import os
  from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torchmetrics.functional.text.bert import bert_score
  from torchmetrics.functional.text.bleu import bleu_score
  from torchmetrics.functional.text.cer import char_error_rate
  from torchmetrics.functional.text.chrf import chrf_score
  from torchmetrics.functional.text.eed import extended_edit_distance
  from torchmetrics.functional.text.infolm import _ALLOWED_INFORMATION_MEASURE_LITERAL
  from torchmetrics.functional.text.infolm import infolm
  from torchmetrics.functional.text.mer import match_error_rate
  from torchmetrics.functional.text.perplexity import perplexity
  from torchmetrics.functional.text.rouge import rouge_score
  from torchmetrics.functional.text.sacre_bleu import sacre_bleu_score
  from torchmetrics.functional.text.squad import squad
  from torchmetrics.functional.text.ter import translation_edit_rate
  from torchmetrics.functional.text.wer import word_error_rate
  from torchmetrics.functional.text.wil import word_information_lost
  from torchmetrics.functional.text.wip import word_information_preserved
  from torchmetrics.utilities.imports import _TRANSFORMERS_GREATER_EQUAL_4_4
  from torchmetrics.utilities.prints import _deprecated_root_import_func
label_desc: Use image node_id 1 for calling a global function with example usage: _bert_score(preds, target, model_name_or_path, num_layers, all_layers, model, user_tokenizer, user_forward_fn, verbose, idf, device, max_length, batch_size, num_threads, return_hash, lang, rescale_with_baseline, baseline_path, baseline_url) and returns: bert_score