code: def dataset(self, feature_extractor=None):
      if feature_extractor is None:
          feature_extractor = AutoFeatureExtractor.from_pretrained(
              self.feature_extractor_name
          )
  
      def transform(example_batch):
          inputs = feature_extractor(
              [x for x in example_batch["img"]], return_tensors="pt"
          )
  
          inputs["labels"] = example_batch["label"]
          return inputs
  
      dataset = datasets.load_dataset(
          "./tests/fixtures/samples/cifar10",
          data_dir="./tests/fixtures/samples/cifar10",
          split="train",
      )
      dataset = dataset.with_transform(transform)
  
      return dataset
  
imports: import random
  import datasets
  import torch
  import adapters
  from adapters import AutoAdapterModel
  from transformers import AutoFeatureExtractor, AutoTokenizer, GlueDataset, GlueDataTrainingArguments
  from transformers.testing_utils import torch_device
label_desc: Use image node_id 3 for calling the VisionAdapterTestBase obj's underlying member method code with example usage: obj.dataset(feature_extractor) and returns: dataset, inputs