code: def test_segment_ids(caplog=None):
      if caplog:
          caplog.set_level(logging.CRITICAL)
      lang_model = "microsoft/MiniLM-L12-H384-uncased"
  
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path=lang_model
      )
  
      processor = TextPairRegressionProcessor(
          tokenizer=tokenizer,
          label_list=None,
          metric="f1_macro",
          max_seq_len=128,
          train_filename="sample.tsv",
          dev_filename="sample.tsv",
          test_filename=None,
          data_dir=Path("samples/text_pair"),
          delimiter="\t",
      )
  
      dicts = processor.file_to_dicts(
          file="samples/text_pair/sample.tsv"
      )
      dataset, tensornames, _, baskets = processor.dataset_from_dicts(
          dicts=dicts, return_baskets=True, debug=True
      )
      assert set(
          baskets[0].samples[0].features[0]["segment_ids"]
      ) == set([0, 1])
      assert (
          tokenizer.sep_token
          in baskets[0].samples[0].tokenized["tokens"]
      )
  
file_path: FARM/test/test_text_pair.py
imports: import logging
  from pathlib import Path
  import numpy
  from farm.data_handler.data_silo import DataSilo
  from farm.data_handler.processor import TextPairClassificationProcessor, TextPairRegressionProcessor
  from farm.experiment import initialize_optimizer
  from farm.infer import Inferencer
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import RegressionHead, TextClassificationHead
  from farm.modeling.tokenization import Tokenizer
  from farm.train import Trainer
  from farm.utils import set_all_seeds, initialize_device_settings
label_desc: Use image node_id 3 for calling a global function with example usage: test_segment_ids(caplog) without return types