code: def test_inference_optical_flow(self):
      model = PerceiverForOpticalFlow.from_pretrained(
          "deepmind/optical-flow-perceiver"
      )
      model.to(torch_device)
  
      # prepare inputs
      image1, image2 = prepare_optical_flow_images()
      img1 = normalize(np.array(image1))
      img2 = normalize(np.array(image1))
  
      # stack images
      img1 = torch.tensor(np.moveaxis(img1, -1, 0))
      img2 = torch.tensor(np.moveaxis(img2, -1, 0))
      images = torch.stack([img1, img2], dim=0)
  
      # extract 3x3 patches
      patch_size = model.config.train_size
  
      inputs = images[..., : patch_size[0], : patch_size[1]].unsqueeze(
          0
      )
      batch_size, _, C, H, W = inputs.shape
      patches = extract_image_patches(
          inputs.view(batch_size * 2, C, H, W), kernel=3
      )
      _, C, H, W = patches.shape
      patches = patches.view(batch_size, -1, C, H, W).float()
  
      # forward pass
      with torch.no_grad():
          outputs = model(inputs=patches.to(torch_device))
      logits = outputs.logits
  
      # verify logits
      expected_shape = torch.Size((1, 368, 496, 2))
      self.assertEqual(logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [[0.0025, -0.0050], [0.0025, -0.0049], [0.0025, -0.0048]],
              [[0.0026, -0.0049], [0.0026, -0.0048], [0.0026, -0.0047]],
              [[0.0026, -0.0049], [0.0026, -0.0048], [0.0026, -0.0046]],
          ],
          device=torch_device,
      )
  
      self.assertTrue(
          torch.allclose(
              logits[0, :3, :3, :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 5 for calling the PerceiverModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_optical_flow() without return types