code: def dataset(self, tokenizer=None):
      # setup tokenizer
      if tokenizer is None:
          tokenizer = AutoTokenizer.from_pretrained(
              self.tokenizer_name, use_fast=False
          )
          if tokenizer.pad_token is None:
              tokenizer.pad_token = tokenizer.eos_token
  
      def preprocess_function(examples):
          inputs = examples["document"]
          targets = examples["summary"]
          inputs = ["Summarize: " + inp for inp in inputs]
          model_inputs = tokenizer(
              inputs,
              padding="max_length",
              truncation=True,
              max_length=128,
          )
  
          # Setup the tokenizer for targets
          with tokenizer.as_target_tokenizer():
              labels = tokenizer(
                  targets,
                  padding="max_length",
                  truncation=True,
                  max_length=128,
              )
  
          # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
          # padding in the loss.
          labels["input_ids"] = [
              [
                  (l if l != tokenizer.pad_token_id else -100)
                  for l in label
              ]
              for label in labels["input_ids"]
          ]
  
          model_inputs["labels"] = labels["input_ids"]
          return model_inputs
  
      data_args = {
          "task_name": "xsum",
          "path": "./tests/fixtures/samples/xsum/sample.json",
      }
      dataset = load_dataset("json", data_files=data_args["path"])
      train_dataset = dataset["train"]
      train_dataset = train_dataset.map(
          preprocess_function,
          batched=True,
          desc="Running tokenizer on train dataset",
      )
      return train_dataset
  
imports: import unittest
  from datasets import load_dataset
  from transformers import AutoTokenizer, BertGenerationConfig
  from transformers.testing_utils import require_torch
  from .composition.test_parallel import ParallelAdapterInferenceTestMixin, ParallelTrainingMixin
  from .methods import BottleneckAdapterTestMixin, CompacterTestMixin, IA3TestMixin, LoRATestMixin, PrefixTuningTestMixin, PromptTuningTestMixin, UniPELTTestMixin
  from .test_adapter import AdapterTestBase, make_config
  from .test_adapter_backward_compability import CompabilityTestMixin
  from .test_adapter_conversion import ModelClassConversionTestMixin
  from .test_adapter_embeddings import EmbeddingTestMixin
  from .test_adapter_fusion_common import AdapterFusionModelTestMixin
  from .test_adapter_heads import PredictionHeadModelTestMixin
label_desc: Use image node_id 2 for calling the BertGenerationAdapterTestBase obj's underlying member method code with example usage: obj.dataset(tokenizer) and returns: train_dataset, model_inputs