code: def test_atan2_backward(self):
      # have to go forward before we can go backward
      a = Tensor.randn(4, 4, requires_grad=True).permute(1, 0)
      b = Tensor.randn(4, 4, requires_grad=True).permute(1, 0)
      c = ATan2.apply(a, b)
  
      # run the backward pass
      c.mean().backward()
      assert (
          a.grad is not None and b.grad is not None
      ), "tinygrad didn't compute gradients"
      print(a.grad.numpy())
      print(b.grad.numpy())
  
      # check the backward pass (in torch)
      import torch
  
      ta, tb = torch.tensor(
          a.numpy(), requires_grad=True
      ), torch.tensor(b.numpy(), requires_grad=True)
      tc = torch.atan2(ta, tb)
      tc.mean().backward()
      assert (
          ta.grad is not None and tb.grad is not None
      ), "torch didn't compute gradients"
      np.testing.assert_allclose(
          a.grad.numpy(), ta.grad.numpy(), atol=1e-5
      )
      np.testing.assert_allclose(
          b.grad.numpy(), tb.grad.numpy(), atol=1e-5
      )
  
imports: import unittest
  import numpy
  from typing import Optional, Tuple
  from tinygrad.helpers import prod
  from tinygrad.dtype import dtypes
  from tinygrad.lazy import Buffer, create_lazybuffer
  from tinygrad.device import CompiledASTRunner, Device
  from tinygrad.shape.shapetracker import ShapeTracker
  from tinygrad.ops import LoadOps, BinaryOps
  from tinygrad.lazy import LazyBuffer
  from tinygrad.tensor import Function
  from tinygrad.tensor import Tensor
label_desc: Use image node_id 2 for calling the TestCustomFunction obj's underlying member method code with example usage: obj.test_atan2_backward() without return types