code: def test_gradcheck(self):
      W = np.random.RandomState(1337).random((10, 5)).astype(np.float32)
      x = np.random.RandomState(7331).random((1, 10)).astype(np.float32)
  
      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()
  
      self.assertTrue(gradcheck(tiny_func, tiny_x, eps=1e-3))
  
      # coarse approx. since a "big" eps and the non-linearities of the model
      self.assertFalse(gradcheck(tiny_func, tiny_x, eps=1e-5))
  
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 8 for calling the TestTinygrad obj's underlying member method code with example usage: obj.test_gradcheck() and returns: x