the error message looks like you are trying to pass a quantized input to BN, but BN is not quantized.  So, you’d need to either fuse it to the preceding module, quantize it, or make sure the input is converted to floating point.  Here is a toy example of expected behavior:
import torch
import torch.nn as nn

class M(nn.Module):
    def __init__(self):
        super(M, self).__init__()
        self.quant = torch.quantization.QuantStub()
        self.conv1 = nn.Conv2d(1, 1, 1)
        self.bn1 = nn.BatchNorm2d(1)
        self.conv2 = nn.Conv2d(1, 1, 1)
        self.bn2 = nn.BatchNorm2d(1)
    
    def forward(self, x):
        x = self.quant(x)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        return x
    
m = M()
m.qconfig = torch.quantization.default_qconfig
m.eval()

torch.quantization.fuse_modules(
    m, 
    [
        ['conv1', 'bn1'], # fuse bn1 into conv1
        # for example's sake, don't fuse conv2 and bn2
    ],
    inplace=True)

torch.quantization.prepare(m, inplace=True)

# toy calibration
data = torch.randn(32, 1, 16, 16)
m(data)

torch.quantization.convert(m, inplace=True)
# self.bn1 was fused with conv1 earlier
# self.bn2 will be QuantizedBatchNorm2d
print(m)