Yes it is. I am not sure what exactly your model is, but here is an example on how you could make a model with two inputs in the forward
class SomeAwesomeModel(nn.Module):
    def __init__(self):
        self.quant_x = torch.quantization.QuantStub()
        self.quant_y = torch.quantization.QuantStub()
        self.func = nn.quantized.FloatFunctional()
        self.dequant = torch.quantization.DeQuantStub()
 
   def forward(self, x, y):
        qx = self.quant_x(x)
        qy = self.quant_y(y)
        qz = self.func.add(qx, qy)
        z = self.dequant(qz)
        return z

The model above expect two floating point tensors as input, quantizes them, and after performing some funciton, returns the dequantized version.