code: def get_input_samples(self, shape=None, config=None):
      shape = shape or self.default_input_samples_shape
      total_dims = 1
      for dim in shape:
          total_dims *= dim
      values = []
      for _ in range(total_dims):
          values.append(random.random())
      pixel_values = (
          torch.tensor(
              data=values, dtype=torch.float, device=torch_device
          )
          .view(shape)
          .contiguous()
      )
      in_data = {"pixel_values": pixel_values}
  
      return in_data
  
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 1 for calling the VisionAdapterTestBase obj's underlying member method code with example usage: obj.get_input_samples(shape, config) and returns: in_data