code: def test_general(self):
      tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
      data_args = GlueDataTrainingArguments(
          task_name="mrpc",
          data_dir="./hf_transformers/tests/fixtures/tests_samples/MRPC",
          overwrite_cache=True,
      )
      train_dataset = GlueDataset(
          data_args, tokenizer=tokenizer, mode="train"
      )
  
      model = AutoAdapterModel.from_config(self.get_model_config())
  
      model.add_classification_head("task", num_labels=3)
  
      # add the adapters to be fused
      model.add_adapter("task")
      model.add_adapter("additional_adapter")
  
      model.train_adapter("task")
      self.assertEqual("task", model.active_head)
      self.assertEqual(Stack("task"), model.active_adapters)
      with TemporaryDirectory() as tempdir:
          training_args = TrainingArguments(
              output_dir=tempdir,
              do_train=True,
              learning_rate=0.1,
              logging_steps=1,
              max_steps=1,
              save_steps=1,
              remove_unused_columns=False,
          )
          trainer = AdapterTrainer(
              model=model,
              args=training_args,
              train_dataset=train_dataset,
          )
  
          trainer.train()
  
          # Check that adapters are actually saved but the full model is not
          files_dir_checkpoint = [
              file_or_dir
              for file_or_dir in os.listdir(
                  os.path.join(tempdir, "checkpoint-1")
              )
          ]
          self.assertTrue("task" in files_dir_checkpoint)
          self.assertTrue("additional_adapter" in files_dir_checkpoint)
          # Check that full model weights are not stored
          self.assertFalse("pytorch_model.bin" in files_dir_checkpoint)
  
          # this should always be false in the adapter trainer
          self.assertFalse(trainer.args.remove_unused_columns)
          self.assertEqual("task", model.active_head)
          self.assertEqual(Stack("task"), model.active_adapters)
  
imports: import os
  import unittest
  from tempfile import TemporaryDirectory
  import torch
  import adapters
  from adapters import AutoAdapterModel
  from adapters.composition import Fuse, Stack
  from adapters.trainer import AdapterTrainer, logger
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, BertConfig, BertForSequenceClassification, GlueDataset, GlueDataTrainingArguments, Trainer, TrainingArguments
  from transformers.testing_utils import require_ray, slow
label_desc: Use image node_id 10 for calling the TestAdapterTrainer obj's underlying member method code with example usage: obj.test_general() without return types