code: def create_and_check_for_image_classification_fourier(
      self, config, inputs, input_mask, sequence_labels, token_labels
  ):
      model = PerceiverForImageClassificationFourier(config=config)
      model.to(torch_device)
      model.eval()
      result = model(
          inputs, attention_mask=input_mask, labels=sequence_labels
      )
      self.parent.assertEqual(
          result.logits.shape, (self.batch_size, self.num_labels)
      )
  
imports: import copy
  import inspect
  import math
  import tempfile
  import unittest
  import warnings
  from typing import Dict, List, Tuple
  import numpy
  from datasets import load_dataset
  from transformers import PerceiverConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 8 for calling the PerceiverModelTester obj's underlying member method code with example usage: obj.create_and_check_for_image_classification_fourier(config, inputs, input_mask, sequence_labels, token_labels) without return types