code: def doc_classification(args):
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
  
      ml_logger = MLFlowLogger(tracking_uri="")
      ml_logger.init_experiment(
          experiment_name="Public_FARM",
          run_name="Run_doc_classification",
      )
  
      set_all_seeds(seed=42)
      save_dir = Path("/opt/ml/model")
      use_amp = None
  
      device, n_gpu = initialize_device_settings(
          use_cuda=True, use_amp=use_amp
      )
  
      # 1.Create a tokenizer
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path=args.base_lm_model,
          do_lower_case=False,
      )
  
      # 2. Create a DataProcessor that handles all the conversion from raw text into a pytorch Dataset
      # Here we load GermEval 2018 Data.
      label_list = ["OTHER", "OFFENSE"]
      metric = "f1_macro"
  
      processor = TextClassificationProcessor(
          tokenizer=tokenizer,
          max_seq_len=args.max_seq_len,
          data_dir=Path("../data/germeval18"),
          label_list=label_list,
          metric=metric,
          label_column_name="coarse_label",
      )
  
      # 3. Create a DataSilo that loads several datasets (train/dev/test), provides DataLoaders for them and calculates a
      #    few descriptive statistics of our datasets
      data_silo = DataSilo(
          processor=processor, batch_size=args.batch_size
      )
  
      # 4. Create an AdaptiveModel
      # a) which consists of a pretrained language model as a basis
      language_model = LanguageModel.load(args.base_lm_model)
      # b) and a prediction head on top that is suited for our task => Text classification
      prediction_head = TextClassificationHead(
          class_weights=data_silo.calculate_class_weights(
              task_name="text_classification"
          ),
          num_labels=len(label_list),
      )
  
      model = AdaptiveModel(
          language_model=language_model,
          prediction_heads=[prediction_head],
          embeds_dropout_prob=0.1,
          lm_output_types=["per_sequence"],
          device=device,
      )
  
      # 5. Create an optimizer
      model, optimizer, lr_schedule = initialize_optimizer(
          model=model,
          learning_rate=3e-5,
          device=device,
          n_batches=len(data_silo.loaders["train"]),
          n_epochs=args.n_epochs,
          use_amp=use_amp,
      )
  
      # 6. Feed everything to the Trainer, which keeps care of growing our model into powerful plant and evaluates it from time to time
      trainer = Trainer(
          model=model,
          optimizer=optimizer,
          data_silo=data_silo,
          epochs=args.n_epochs,
          n_gpu=n_gpu,
          lr_schedule=lr_schedule,
          evaluate_every=args.evaluate_every,
          device=device,
      )
  
      # 7. Let it grow
      trainer.train()
  
      # 8. Hooray! You have a model. Store it:
      model.save(save_dir)
      processor.save(save_dir)
  
file_path: FARM/tutorials/sagemaker/source/doc_classification.py
imports: import argparse
  import logging
  from pathlib import Path
  from farm.data_handler.data_silo import DataSilo
  from farm.data_handler.processor import TextClassificationProcessor
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.optimization import initialize_optimizer
  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, MLFlowLogger, initialize_device_settings
label_desc: Use image node_id 1 for calling a global function with example usage: doc_classification(args) without return types