code: def extract_image_patches(x, kernel, stride=1, dilation=1):
      # Do TF 'SAME' Padding
      b, c, h, w = x.shape
      h2 = math.ceil(h / stride)
      w2 = math.ceil(w / stride)
      pad_row = (h2 - 1) * stride + (kernel - 1) * dilation + 1 - h
      pad_col = (w2 - 1) * stride + (kernel - 1) * dilation + 1 - w
      x = torch.nn.functional.pad(
          x,
          (
              pad_row // 2,
              pad_row - pad_row // 2,
              pad_col // 2,
              pad_col - pad_col // 2,
          ),
      )
  
      # Extract patches
      patches = x.unfold(2, kernel, stride).unfold(3, kernel, stride)
      patches = patches.permute(0, 4, 5, 1, 2, 3).contiguous()
  
      return patches.view(b, -1, patches.shape[-2], patches.shape[-1])
  
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 a global function with example usage: extract_image_patches(x, kernel, stride, dilation) and returns: patches