my model as following:
from torch.quantization import QuantStub, DeQuantStub

class dehaze_net(nn.Module):
    
	def __init__(self):
		super(dehaze_net, self).__init__()

		self.relu = nn.ReLU(inplace=False)
	
		self.e_conv1 = nn.Conv2d(3,3,1,1,0,bias=True) 
		self.e_conv2 = nn.Conv2d(3,3,3,1,1,bias=True) 
		self.e_conv3 = nn.Conv2d(6,3,5,1,2,bias=True) 
		self.e_conv4 = nn.Conv2d(6,3,7,1,3,bias=True) 
		self.e_conv5 = nn.Conv2d(12,3,3,1,1,bias=True)
		self.skip_add = nn.quantized.FloatFunctional()
		self.quant = QuantStub() 
		self.dequant = DeQuantStub() 
        # weight initialization
		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				nn.init.kaiming_normal_(m.weight, mode='fan_out')
				if m.bias is not None:
					nn.init.zeros_(m.bias)
			elif isinstance(m, nn.BatchNorm2d):
				nn.init.ones_(m.weight)
				nn.init.zeros_(m.bias)
			elif isinstance(m, nn.Linear):
				nn.init.normal_(m.weight, 0, 0.01)
				nn.init.zeros_(m.bias)
		
	def forward(self, x):
		x = self.quant(x)
		source = []
		source.append(x)

		x1 = self.relu(self.e_conv1(x))
		x2 = self.relu(self.e_conv2(x1))

		concat1 =self.skip_add.cat((x1,x2), 1)
		x3 = self.relu(self.e_conv3(concat1))

		concat2 = self.skip_add.cat((x2, x3), 1)
		x4 = self.relu(self.e_conv4(concat2))

		concat3 = self.skip_add.cat((x1,x2,x3,x4),1)
		x5 = self.relu(self.e_conv5(concat3))

		#clean_image = self.relu(self.skip_add.add((x5 * x) - x5, 1)) 
		clean_image = self.skip_add.add_relu(self.skip_add.add(self.skip_add.mul(x5,x),-x5), 1)

		clean_image = self.dequant(clean_image)
		return clean_image

i load the pretrained model ,and predict the image ,it is right .as follows
def load_model(model_file):
    model = dehaze_net()
    state_dict = torch.load(model_file)
    model.load_state_dict(state_dict)
    model.to('cpu')
    return model
myModel = load_model(float_model_file).to('cpu')
myModel.eval()

myModel.qconfig = torch.quantization.default_qconfig
print(myModel.qconfig)
torch.quantization.prepare(myModel, inplace=True)
validation(myModel, data_loader_test)

but when i excute the following codes ,it give some errors:
torch.quantization.convert(myModel, inplace=True)
validation(myModel, data_loader_test)

errors:
<ipython-input-44-674cc7c0f6b1> in forward(self, x)
     47 
     48                 #clean_image = self.relu(self.skip_add.add((x5 * x) - x5, 1))
---> 49                 clean_image = self.skip_add.add_relu(self.skip_add.add(self.skip_add.mul(x5,x),-x5), 1)
     50 
     51                 clean_image = self.dequant(clean_image)

RuntimeError: No function is registered for schema aten::empty.memory_format(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor on tensor type QuantizedCPUTensorId; available functions are CPUTensorId, CUDATensorId, MkldnnCPUTensorId, SparseCPUTensorId, SparseCUDATensorId, VariableTensorId

thanks