I am trying to quantize a model which has upsampling layers at specific parts of network but unable to quantize it due to this Error.

Expected a value of type ‘Tensor’ for argument ‘target_size’ but instead found type ‘List[int]’.
Inferred ‘target_size’ to be of type ‘Tensor’ because it was not annotated with an explicit type.

Implementation of the upsampling layer
class Upsample(nn.Module):
    def __init__(self):
        super(Upsample, self).__init__()

    def forward(self, x, target_size):
        # assert (x.data.dim() == 4)

        _, _, tH, tW = target_size[0], target_size[1], target_size[2], target_size[3]

        B = x.data.size(0)
        C = x.data.size(1)
        H = x.data.size(2)
        W = x.data.size(3)

        return x.view(B, C, H, 1, W, 1).expand(B, C, H, tH // H, W, tW // W).contiguous().view(B, C, tH, tW)


Upsampling function usage
up = self.upsample1(x7, downsample4.size())

Quantization code
model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
print(model.qconfig)

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

print('Post Training Quantization Prepare : Inserting Observers')
print('\n Downsampling1 Block: After observer insertion \n\n', model.down1.conv1)

torch.quantization.convert(model, inplace=True)
print("Post Training Quantization : Convert Done!")
print("\n Downsampling1Block: After quantization \n\n", model.down1.conv1)
torch.jit.save(torch.jit.script(model), quantized_model_path)

This is my first time trying to quantize a model in pytorch so I am totally clueless on how to solve this. Thanks in advance.