Issue with RationalHat layer, local variable 'LVinit' referenced before assignment

[RandomAnass]

When I use the layer "RationalHat", I get the following error:

UnboundLocalError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_33568/2646110522.py in
65 rho = tf.keras.Sequential([tf.keras.layers.Dense(1, activation="sigmoid")])
66 model = None
---> 67 model = PerslayModel(name="PersLay", diagdim=2, perslay_parameters=perslay_parameters, rho=rho)
68 model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
69 print(optimizer,perm_op,pweight,layer)

~\anaconda3\envs\downgrade\lib\site-packages\perslay\perslay.py in init(self, name, diagdim, perslay_parameters, rho)
159 LMinit, LRinit = plp["lmean_init"], plp["lr_init"]
160 LMiv = LMinit if not callable(LMinit) else LMinit([self.diagdim, plp["lnum"]])
--> 161 LRiv = LRinit if not callable(LRinit) else LVinit([1])
162 LM = tf.Variable(name=Lname+"-M", initial_value=LMiv, trainable=Ltrain)
163 LR = tf.Variable(name=Lname+"-R", initial_value=LRiv, trainable=Ltrain)

UnboundLocalError: local variable 'LVinit' referenced before assignment

When I checked the perslay.py file, I found :

elif layer == "RationalHat":
    LMinit, LRinit = plp["lmean_init"], plp["lr_init"]
    LMiv = LMinit if not callable(LMinit) else LMinit([self.diagdim, plp["lnum"]])
    LRiv = LRinit if not callable(LRinit) else LVinit([1])
    LM = tf.Variable(name=Lname+"-M", initial_value=LMiv, trainable=Ltrain)
    LR = tf.Variable(name=Lname+"-R", initial_value=LRiv, trainable=Ltrain)
    self.vars[nf].append([LM, LR])

Where LVinit is used before being defined, unlike the other layers where its defined at the beginning (after elif).

I dont know if I am not using the layer correctly (for example if I had to define an other layer before it). But I tried the same code with all the other layers (except the image layer) and it worked without an error.