code: def test_jacobian(self):
      W = (
          np.random.RandomState(42069)
          .random((10, 5))
          .astype(np.float32)
      )
      x = (
          np.random.RandomState(69420)
          .random((1, 10))
          .astype(np.float32)
      )
  
      torch_x = torch.tensor(x, requires_grad=True)
      torch_W = torch.tensor(W, requires_grad=True)
  
      def torch_func(x):
          return torch.nn.functional.log_softmax(
              x.matmul(torch_W).relu(), dim=1
          )
  
      PJ = (
          torch.autograd.functional.jacobian(torch_func, torch_x)
          .squeeze()
          .numpy()
      )
  
      tiny_x = Tensor(x, requires_grad=True)
      tiny_W = Tensor(W, requires_grad=True)
  
      def tiny_func(x):
          return x.dot(tiny_W).relu().log_softmax()
  
      J = jacobian(tiny_func, tiny_x)
      NJ = numerical_jacobian(tiny_func, tiny_x)
  
      np.testing.assert_allclose(PJ, J, atol=1e-5)
      np.testing.assert_allclose(PJ, NJ, atol=1e-3)
  
imports: import numpy
  import torch
  import unittest, copy
  import mmap
  from tinygrad import Tensor, Device, dtypes
  from tinygrad.helpers import temp
  from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
label_desc: Use image node_id 7 for calling the TestTinygrad obj's underlying member method code with example usage: obj.test_jacobian() and returns: torch, x