def test_conv2d_with_bias():

    def conv2d_with_bias(inputs, filters, bias):
        conv = F.conv2d(inputs, filters, bias)
        return F.relu(conv)
    inputs = torch.randn(1, 4, 5, 5)
    filters = torch.randn(8, 4, 3, 3)
    bias = torch.randn(8)
    jitVsGlow(conv2d_with_bias, inputs, filters, bias)
def test_conv2d_with_bias():

    def conv2d_with_bias(inputs, filters, bias):
        conv = F.conv2d(inputs, filters, bias)
        return F.relu(conv)
    inputs = torch.randn(1, 4, 5, 5)
    filters = torch.randn(8, 4, 3, 3)
    bias = torch.randn(8)
    jitVsGlow(conv2d_with_bias, inputs, filters, bias)