def METHOD_NAME(self):
    """
    Test of the PyTorch 2D batchnorm Node with weights and biases on Glow.
    """
    class SimpleQuantizedBatchNorm(nn.Module):
        def __init__(
            self,
            C,
            weight,
            bias,
            running_mean,
            running_var,
            in_scale,
            in_zero_point,
            out_scale,
            out_zero_point,
        ):
            super(SimpleQuantizedBatchNorm, self).__init__()
            self.qconfig = my_qconfig
            self.batchnorm = nn.BatchNorm2d(C)
            self.batchnorm.scale = out_scale
            self.batchnorm.zero_point = out_zero_point
            self.batchnorm.weight = nn.Parameter(weight)
            self.batchnorm.bias = nn.Parameter(bias)
            self.batchnorm.running_mean = running_mean
            self.batchnorm.running_var = running_var
            self.relu = nn.ReLU()
            self.q = torch.ao.quantization.QuantStub()
            self.q.scale = in_scale
            self.q.zero_point = in_zero_point
            self.dq = torch.ao.quantization.DeQuantStub()
        def forward(self, x):
            qx = self.q(x)
            qy = self.batchnorm(qx)
            y = self.dq(qy)
            return y
    C = 11
    in_scale = 0.234
    out_scale = 0.003
    in_zero_point = -10
    out_zero_point = -5
    weight = torch.ones(C) + torch.rand(C) * 0.001
    bias = torch.rand(C) * 0.0001
    running_mean = torch.zeros(C)
    running_var = torch.ones(C)
    inputs = torch.randn((6, C, 33, 42), requires_grad=False)
    model = SimpleQuantizedBatchNorm(
        C,
        weight,
        bias,
        running_mean,
        running_var,
        in_scale,
        in_zero_point,
        out_scale,
        out_zero_point,
    )
    model.eval()
    utils.compare_tracing_methods(
        model,
        inputs,
        skip_to_glow=True,
    )