code: def test_backward_pass_diamond_model(self):
      def test_tinygrad():
          u = Tensor(U_init, requires_grad=True)
          v = Tensor(V_init, requires_grad=True)
          w = Tensor(W_init, requires_grad=True)
          x = u.mul(v).relu()
          y = u.mul(w).relu()
          out = x.add(y).mul(y).relu()
          out = out.log_softmax()
          out = out.sum()
          out.backward()
          return (
              out.numpy(),
              u.grad.numpy(),
              v.grad.numpy(),
              w.grad.numpy(),
          )
  
      def test_pytorch():
          u = torch.tensor(U_init, requires_grad=True)
          v = torch.tensor(V_init, requires_grad=True)
          w = torch.tensor(W_init, requires_grad=True)
          x = u.mul(v).relu()
          y = u.mul(w).relu()
          out = x.add(y).mul(y).relu()
          out = torch.nn.functional.log_softmax(out, dim=1)
          out = out.sum()
          out.backward()
          return out.detach().numpy(), u.grad, v.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 4 for calling the TestTinygrad obj's underlying member method code with example usage: obj.test_backward_pass_diamond_model() and returns: out,  u,  v,  w, out,  u,  v,  w