Unsuccessful TensorSliceReader constructor

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.datasets import mnist
import pickle
 
class NeuralNetworks:
    def __init__(self, epochs):   
        self.epochs = epochs            
        
    def __getstate__(self) -> dict:
        state = {}
        state["epochs"] = self.epochs
        state["history"] = self.history
        state["score"] = self.score
        return state
 
    def __setstate__(self, state: dict):
        self.epochs = state["epochs"]
        self.history = state["history"]
        self.score = state["score"]
    
    def train(self):     
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
        
        x_train = np.expand_dims(x_train.astype("float32") / 255, -1)
        x_test = np.expand_dims(x_test.astype("float32") / 255, -1)        
        y_train = to_categorical(y_train, 10)
        y_test = to_categorical(y_test, 10)        
 
        model = Sequential()
        model.add(Flatten(input_shape=(28, 28, 1)))
        model.add(Dense(200, activation = 'relu'))
        model.add(Dense(100, activation = 'relu'))
        model.add(Dense(10, activation = 'softmax'))         
 
        model.compile(loss = 'binary_crossentropy', optimizer='adam',  metrics=['accuracy'])
        self.history = model.fit(x_train, y_train, epochs = self.epochs, verbose = 2, validation_split = 0.1, shuffle = True)
        self.score = model.evaluate(x_test, y_test, verbose = 0) 
 
NN = NeuralNetworks(epochs = 10)
NN.train()
 
with open('test.pkl', "wb") as fp:
    pickle.dump(NN, fp)
 
with open('test.pkl', "rb") as fp:
    nn = pickle.load(fp)