docstr: Check if the gradients of the transforms are correct w.r.t to the input data.
docstr_tok: ["Check","if","the","gradients","of","the","transforms","are","correct","w.r.t","to","the","input","data","."]
code: def test_grad_zca_individual_transforms(self, device, dtype):
      """Check if the gradients of the transforms are correct w.r.t to the input data."""
      data = torch.tensor(
          [[2, 0], [0, 1], [-2, 0], [0, -1]], device=device, dtype=dtype
      )
  
      data = utils.tensor_to_gradcheck_var(data)
  
      def zca_T(x):
          return kornia.enhance.zca_mean(x)[0]
  
      def zca_mu(x):
          return kornia.enhance.zca_mean(x)[1]
  
      def zca_T_inv(x):
          return kornia.enhance.zca_mean(x, return_inverse=True)[2]
  
      assert gradcheck(zca_T, (data,), raise_exception=True)
      assert gradcheck(zca_mu, (data,), raise_exception=True)
      assert gradcheck(zca_T_inv, (data,), raise_exception=True)
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import BaseTester
label_desc: Use image node_id 4 for calling the TestZCA obj's underlying member method code with example usage: obj.test_grad_zca_individual_transforms(device, dtype) and returns: kornia, kornia, kornia