code: def get_input_samples(self, shape=None, vocab_size=5000, 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.randint(0, vocab_size - 1))
      input_ids = (
          torch.tensor(
              data=values, dtype=torch.long, device=torch_device
          )
          .view(shape)
          .contiguous()
      )
      # this is needed e.g. for BART
      if (
          config
          and config.eos_token_id is not None
          and config.eos_token_id < vocab_size
      ):
          input_ids[input_ids == config.eos_token_id] = random.randint(
              0, config.eos_token_id - 1
          )
          input_ids[:, -1] = config.eos_token_id
      in_data = {"input_ids": input_ids}
  
      if config and config.is_encoder_decoder:
          in_data["decoder_input_ids"] = input_ids.clone()
      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 2 for calling the AdapterTestBase obj's underlying member method code with example usage: obj.get_input_samples(shape, vocab_size, config) and returns: in_data