So the issue is that I was passing floatTensor to a now torch.qint8, and attempting to cast it to a Parameter. The solution is to put an if statement like so:
class Block(torch.nn.Module):
    def __init__(self):
        super(Block, self).__init__()
        self.conv1 = torch.nn.Conv2d(1,1,3, padding=1, bias=False)
    def forward(self, x, conv_parameter):
        if(isinstance(self.conv1, torch.nn.quantized.Conv2d)):
            return self.conv1(x)
        else:
            self.conv1.weight = torch.nn.parameter.Parameter(conv_parameter) 
        return self.conv1(x)

which works since my passed in conv_parameter should be equal to the weight stored in self.conv1, so we can just ignore the conversion to Parameter when using the quantized network.