code: def test_doc_classification(caplog):
      if caplog:
          caplog.set_level(logging.CRITICAL)
  
      set_all_seeds(seed=42)
      device, n_gpu = initialize_device_settings(use_cuda=False)
      n_epochs = 1
      batch_size = 1
      evaluate_every = 2
      lang_model = "distilbert-base-german-cased"
  
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path=lang_model, do_lower_case=False
      )
  
      processor = TextClassificationProcessor(
          tokenizer=tokenizer,
          max_seq_len=8,
          data_dir=Path("samples/doc_class"),
          train_filename=Path("train-sample.tsv"),
          label_list=["OTHER", "OFFENSE"],
          metric="f1_macro",
          dev_filename="test-sample.tsv",
          test_filename=None,
          dev_split=0.0,
          label_column_name="coarse_label",
      )
  
      data_silo = DataSilo(processor=processor, batch_size=batch_size)
  
      language_model = DistilBert.load(lang_model)
      prediction_head = TextClassificationHead(num_labels=2)
      model = AdaptiveModel(
          language_model=language_model,
          prediction_heads=[prediction_head],
          embeds_dropout_prob=0.1,
          lm_output_types=["per_sequence"],
          device=device,
      )
  
      model, optimizer, lr_schedule = initialize_optimizer(
          model=model,
          learning_rate=2e-5,
          n_batches=len(data_silo.loaders["train"]),
          n_epochs=1,
          device=device,
          schedule_opts=None,
      )
  
      trainer = Trainer(
          model=model,
          optimizer=optimizer,
          data_silo=data_silo,
          epochs=n_epochs,
          n_gpu=n_gpu,
          lr_schedule=lr_schedule,
          evaluate_every=evaluate_every,
          device=device,
      )
  
      trainer.train()
  
      save_dir = Path("testsave/doc_class")
      model.save(save_dir)
      processor.save(save_dir)
  
      del model
      del processor
      del optimizer
      del data_silo
      del trainer
  
      basic_texts = [
          {"text": "Malte liebt Berlin."},
          {
              "text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei."
          },
      ]
  
      inf = Inferencer.load(save_dir, batch_size=2, num_processes=0)
      result = inf.inference_from_dicts(dicts=basic_texts)
      assert isinstance(
          result[0]["predictions"][0]["probability"], np.float32
      )
      del inf
  
file_path: FARM/test/test_doc_classification_distilbert.py
imports: from pathlib import Path
  import logging
  import numpy
  from farm.data_handler.data_silo import DataSilo
  from farm.data_handler.processor import TextClassificationProcessor
  from farm.modeling.optimization import initialize_optimizer
  from farm.infer import Inferencer
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.language_model import DistilBert
  from farm.modeling.prediction_head import 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 1 for calling a global function with example usage: test_doc_classification(caplog) without return types