code: def test_conv2d_winograd(self):
      BS, C1, H, W = 2, 8, 16, 16
      C2, K, S, P = 8, 3, 1, 1
  
      # create in tinygrad
      layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
      layer.weight.requires_grad = True
      layer.bias.requires_grad = True
  
      # create in torch
      torch_layer = torch.nn.Conv2d(
          C1, C2, kernel_size=K, stride=S, padding=P
      ).eval()
      torch_layer.weight = torch.nn.Parameter(
          torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      )
      torch_layer.bias = torch.nn.Parameter(
          torch.tensor(layer.bias.numpy(), dtype=torch.float32)
      )
  
      # test
      x = Tensor.uniform(BS, C1, H, W, requires_grad=True)
  
      old_wino = Tensor.wino
      Tensor.wino = True
      z = layer(x)
      Tensor.wino = old_wino
  
      torch_x = torch.tensor(x.numpy(), requires_grad=True)
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(
          z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5
      )
  
      m = z.mean()
      m.backward()
      gw = layer.weight.grad.realize()
      gb = layer.bias.grad.realize()
      gx = x.grad.realize()
  
      torch_z.mean().backward()
      np.testing.assert_allclose(
          gw.numpy(),
          torch_layer.weight.grad.numpy(),
          atol=5e-4,
          rtol=1e-5,
      )
      np.testing.assert_allclose(
          gb.numpy(),
          torch_layer.bias.grad.numpy(),
          atol=5e-4,
          rtol=1e-5,
      )
      np.testing.assert_allclose(
          gx.numpy(), torch_x.grad.numpy(), atol=5e-4, rtol=1e-5
      )
  
imports: import unittest
  import numpy
  import torch
  from tinygrad import Tensor, Device, TinyJit
  from tinygrad.helpers import CI
  from tinygrad.nn import BatchNorm2d, Conv1d, ConvTranspose1d, Conv2d, ConvTranspose2d, Linear, GroupNorm, LayerNorm, LayerNorm2d, Embedding, InstanceNorm
label_desc: Use image node_id 7 for calling the TestNN obj's underlying member method code with example usage: obj.test_conv2d_winograd() without return types