code: def test_param(
      self,
      scale,
      ratio,
      resample,
      align_corners,
      same_on_batch,
      device,
      dtype,
  ):
      _scale = (
          scale
          if isinstance(scale, (list, tuple))
          else nn.Parameter(
              scale.clone().to(device=device, dtype=dtype)
          )
      )
      _ratio = (
          ratio
          if isinstance(ratio, (list, tuple))
          else nn.Parameter(
              ratio.clone().to(device=device, dtype=dtype)
          )
      )
  
      torch.manual_seed(0)
      input = (
          torch.randint(255, (2, 3, 10, 10), device=device, dtype=dtype)
          / 255.0
      )
      aug = RandomResizedCrop(
          (8, 8),
          _scale,
          _ratio,
          resample=resample,
          same_on_batch=same_on_batch,
          align_corners=align_corners,
      )
  
      output = aug(input)
  
      if len(list(aug.parameters())) != 0:
          mse = nn.MSELoss()
          opt = torch.optim.SGD(aug.parameters(), lr=0.1)
          loss = mse(output, torch.ones_like(output) * 2)
          loss.backward()
          opt.step()
  
      if not isinstance(scale, (list, tuple)):
          assert isinstance(aug.scale, torch.Tensor)
          # Assert if param not updated
          assert (
              scale.to(device=device, dtype=dtype) - aug.scale.data
          ).sum() != 0
      if not isinstance(ratio, (list, tuple)):
          assert isinstance(aug.ratio, torch.Tensor)
          # Assert if param not updated
          assert (
              ratio.to(device=device, dtype=dtype) - aug.ratio.data
          ).sum() != 0
  
imports: import pytest
  import torch
  from torch import nn
  from kornia.augmentation import ColorJiggle, ColorJitter, RandomAffine, RandomErasing, RandomMotionBlur, RandomPerspective, RandomResizedCrop, RandomRotation, RandomSharpness
label_desc: Use image node_id 1 for calling the TestRandomResizedCropBackward obj's underlying member method code with example usage: obj.test_param(scale, ratio, resample, align_corners, same_on_batch, device, dtype) without return types