code: def test_backward_pass(self):
      def test_tinygrad():
          x = Tensor(x_init, requires_grad=True)
          W = Tensor(W_init, requires_grad=True)
          m = Tensor(m_init)
          out = x.dot(W).relu()
          out = out.log_softmax()
          out = out.mul(m).add(m).sum()
          out.backward()
          return out.numpy(), x.grad.numpy(), W.grad.numpy()
  
      def test_pytorch():
          x = torch.tensor(x_init, requires_grad=True)
          W = torch.tensor(W_init, requires_grad=True)
          m = torch.tensor(m_init)
          out = x.matmul(W).relu()
          out = torch.nn.functional.log_softmax(out, dim=1)
          out = out.mul(m).add(m).sum()
          out.backward()
          return out.detach().numpy(), x.grad, W.grad
  
      for x, y in zip(test_tinygrad(), test_pytorch()):
          np.testing.assert_allclose(x, y, atol=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 3 for calling the TestTinygrad obj's underlying member method code with example usage: obj.test_backward_pass() and returns: out,  x,  W, out,  x,  W