Hi @Maria_Vazhaeparambil , we have a quick help page about this type of errors: Quantization — PyTorch master documentation 13
In this case, it looks like a quantized tensor is being passed to a floaing point kernel.  You could fix it by a couple of ways:

convert it to fp32 before passing to the layer (by passing through torch.quantization.DeQuantStub()

or, if you are quantizing the network, use torch.quantization.FloatFunctional for adds