code: def predict_on_batch(
      text_batch: Sequence[str],
      classifier,
      output_formatter: Callable[[Dict[str, Any]], float],
  ) -> Sequence[float]:
      """Return prediction of huggingface Pipeline classifier."""
      # TODO: make this way smarter, and not just a hack. Count tokens, for a start. Then not just sample sentences.
      # If text is longer than classifier context window, sample it:
      text_list_to_predict = []
      reduced_batch_size = len(
          text_batch
      )  # Initialize the reduced batch size
      retry_count = 0
  
      for text in text_batch:
          if len(text) > MAX_CHARS:
              sentences = _sample_for_property(
                  text, mode="sentences", limit=10, return_as_list=True
              )
              text_to_use = ""
              for sentence in sentences:
                  if len(text_to_use) + len(sentence) > MAX_CHARS:
                      break
                  text_to_use += sentence + ". "
  
              # if even one sentence is too long, use part of the first one:
              if len(text_to_use) == 0:
                  text_to_use = cut_string(sentences[0], MAX_CHARS)
              text_list_to_predict.append(text_to_use)
          else:
              text_list_to_predict.append(text)
  
      while reduced_batch_size >= 1:
          try:
              if reduced_batch_size == 1 or retry_count == 3:
                  results = []
                  for text in text_list_to_predict:
                      try:
                          v = classifier(text)[0]
                          results.append(output_formatter(v))
                      except Exception:  # pylint: disable=broad-except
                          results.append(np.nan)
                  return results  # Return the results if prediction is successful
  
              v_list = classifier(
                  text_list_to_predict, batch_size=reduced_batch_size
              )
              results = []
  
              for v in v_list:
                  results.append(output_formatter(v))
  
              return results  # Return the results if prediction is successful
  
          except Exception:  # pylint: disable=broad-except
              reduced_batch_size = max(
                  reduced_batch_size // 2, 1
              )  # Reduce the batch size by half
              retry_count += 1
  
      return [np.nan] * len(text_batch)
  
imports: import pathlib
  import pickle
  import re
  import string
  import warnings
  from collections import defaultdict
  from importlib.util import find_spec
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  import textblob
  import torch.cuda
  from nltk import corpus
  from nltk import download
  from nltk import sent_tokenize, word_tokenize
  from tqdm import tqdm
  from typing_extensions import TypedDict
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.nlp.utils.text import cut_string, hash_text, normalize_text, remove_punctuation
  from deepchecks.nlp.utils.text_properties_models import get_cmudict_dict, get_fasttext_model, get_transformer_pipeline
  from deepchecks.utils.function import run_available_kwargs
  from deepchecks.utils.strings import SPECIAL_CHARACTERS, format_list
  from deepchecks.utils.validation import is_sequence_not_str
label_desc: Use image node_id 14 for calling a global function with example usage: predict_on_batch(text_batch, classifier, output_formatter) and returns: unknown, results, results