It is like @babababadukeduke has mentioned, you need to first define the model.
Below is the example with your own defined model.
# Custom Model
class TwoLayerNet(torch.nn.Module):
   def __init__(self, D_in, H, D_out):
       """
       In the constructor we instantiate two nn.Linear modules and assign them as
       member variables.
       """
       super(TwoLayerNet, self).__init__()
       self.linear1 = torch.nn.Linear(D_in, H)
       self.linear2 = torch.nn.Linear(H, D_out)

   def forward(self, x):
       """
       In the forward function we accept a Tensor of input data and we must return
       a Tensor of output data. We can use Modules defined in the constructor as
       well as arbitrary operators on Tensors.
       """
       h_relu = self.linear1(x).clamp(min=0)
       y_pred = self.linear2(h_relu)
       return y_pred

# Model Object / Blueprint
model = TwoLayerNet(D_in, H, D_out)


# Load the weights into the model.
model_transfer = model.load_state_dict(torch.load('model.path')) # Weight from same blue print.