code: def test_inference_image_classification_conv(self):
      feature_extractor = PerceiverFeatureExtractor()
      model = (
          PerceiverForImageClassificationConvProcessing.from_pretrained(
              "deepmind/vision-perceiver-conv"
          )
      )
      model.to(torch_device)
  
      # prepare inputs
      image = prepare_img()
      inputs = feature_extractor(
          image, return_tensors="pt"
      ).pixel_values.to(torch_device)
      input_mask = None
  
      # forward pass
      with torch.no_grad():
          outputs = model(inputs=inputs, attention_mask=input_mask)
      logits = outputs.logits
  
      # verify logits
      expected_shape = torch.Size((1, model.config.num_labels))
      self.assertEqual(logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [-1.1186, 0.0554, 0.0897], device=torch_device
      )
  
      self.assertTrue(
          torch.allclose(logits[0, :3], expected_slice, atol=1e-4)
      )
  
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 4 for calling the PerceiverModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_conv() without return types