def METHOD_NAME(self):
    with torch.cuda.amp.autocast(dtype=torch.half):
        out = ConvBiasReLU(self.x, self.conv1.weight, self.conv1.bias.reshape(1, -1, 1, 1), self.conv_pad, self.conv_stride)
        loss = (out.float()**2).sum() / out.numel()
    loss.backward()
    with torch.cuda.amp.autocast(dtype=torch.half):
        out_ = F.relu(self.conv1_(self.x_))
        loss_ = (out_**2).sum() / out_.numel()
    loss_.backward()
    torch.testing.assert_close(out_, out, atol=1e-3, rtol=1e-3, equal_nan=True)
    torch.testing.assert_close(self.conv1_.bias.grad, self.conv1.bias.grad, atol=1e-3, rtol=1e-3, equal_nan=True)
    torch.testing.assert_close(self.conv1_.weight.grad, self.conv1.weight.grad, atol=1e-3, rtol=1e-3, equal_nan=True)
    torch.testing.assert_close(self.x_.grad, self.x.grad, atol=1e-3, rtol=1e-3, equal_nan=True)