code: def prepare_config_and_inputs(
      self,
      num_images=1,
      interpolate_pos_encoding=False,
      image_expansion=0,
  ):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
  
      pixel_values = floats_tensor(
          [
              self.batch_size,
              num_images,
              self.num_channels,
              self.image_size + image_expansion,
              self.image_size + image_expansion,
          ]
      )
      input_mask = None
      if self.use_input_mask:
          input_mask = random_attention_mask(
              [self.batch_size, self.seq_length]
          )
  
      image_attention_mask = random_attention_mask(
          [self.batch_size, self.seq_length, num_images]
      )
  
      config = self.get_config()
      return (
          config,
          input_ids,
          input_mask,
          pixel_values,
          image_attention_mask,
          interpolate_pos_encoding,
      )
  
imports: import unittest
  from parameterized import parameterized
  from transformers import BitsAndBytesConfig, IdeficsConfig, is_torch_available, is_vision_available
  from transformers.testing_utils import TestCasePlus, require_bitsandbytes, require_torch, require_torch_sdpa, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the IdeficsModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs(num_images, interpolate_pos_encoding, image_expansion) and returns: config,  input_ids,  input_mask,  pixel_values,  image_attention_mask,  interpolate_pos_encoding