code: def prepare_data(shape, device, dtype=torch.float32):
      xm = torch.tensor(
          [
              [0, 0, 0, 0, 0],
              [1, 1, 1, 1, 1],
              [2, 2, 2, 2, 2],
              [3, 3, 3, 3, 3],
              [4, 4, 4, 4, 4],
          ],
          device=device,
          dtype=dtype,
      )
      ym = torch.tensor(
          [
              [0, 1, 2, 3, 4],
              [0, 1, 2, 3, 4],
              [0, 1, 2, 3, 4],
              [0, 1, 2, 3, 4],
              [0, 1, 2, 3, 4],
          ],
          device=device,
          dtype=dtype,
      )
  
      x = torch.zeros(shape, device=device, dtype=dtype)
      if len(shape) == 2:
          x = xm
      else:
          x[..., 0, :, :] = xm
          x[..., 1, :, :] = ym
  
      return x
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import BaseTester, assert_close
label_desc: Use image node_id 8 for calling the TestUpscaleDouble obj's underlying member method code with example usage: obj.prepare_data(shape, device, dtype) and returns: x