I have a trained model by me that  I saved using
torch.save(model.state_dict(), PATH)

Now I am testing the model after importing the model as
class Model(torch.nn.Module):
    def __init__(self, model, pool):
        super(Model, self).__init__()
        self.model = model
        self.pool = pool

    def forward(self, sample):
        output = self.model(sample)
        #output = self.pool(output) #GeM
        output =  torch.sum(output.view(output.size(0), output.size(1), -1), dim=2) #SPoC

        output = F.normalize(output, p=2, dim=1) #L2 Normalizartion
        return output

model = Model(model=resnet, pool=gmp)
model.load_state_dict(torch.load(PATH))
model.eval()

You can see, I actually am not using the pool but using something else (pool was used in a different experiment using the same model).
Now I am getting this error
File "test.py", line 123, in <module>
    model.load_state_dict(torch.load(PATH))
File "/home/.../python3.8/site-packages/torch/nn/modules/module.py", line 1406, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Model:
	Missing key(s) in state_dict: "pool.p". 

here PATH = path to the saved model ‘model.pt’
Can anyone explain what the error is and how to solve it?
I checked the forums and found a case where users were using nn.dataparallel or something like that, but I am not using it.