TypeError: 'NoneType' object is not callable, how can i solve this error?(I'm a beginner)

Question

import numpy as np
import matplotlib.pyplot as plt
import os
from PIL import Image
import tensorflow as tf

import keras
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, Input, Conv2DTranspose, Concatenate, BatchNormalization, UpSampling2D
from keras.layers import  Dropout, Activation
from tensorflow.keras.optimizers import Adam, SGD
from keras.layers.advanced_activations import LeakyReLU
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras import backend as K
from tensorflow.keras.utils import plot_model
import glob
import random
import cv2
from random import shuffle
print(tf.__version__)
print(keras.__version__)

def build_callbacks():
        checkpointer = ModelCheckpoint(filepath='unet.h5', verbose=0, save_best_only=True, save_weights_only=True)
        callbacks = [checkpointer, PlotLearning()]
        return callbacks

# inheritance for training process plot 
class PlotLearning(keras.callbacks.Callback):

    def on_train_begin(self, logs={}):
        self.i = 0
        self.x = []
        self.losses = []
        self.val_losses = []
        self.acc = []
        self.val_acc = []
        #self.fig = plt.figure()
        self.logs = []
    def on_epoch_end(self, epoch, logs={}):
        self.logs.append(logs)
        self.x.append(self.i)
        self.losses.append(logs.get('loss'))
        self.val_losses.append(logs.get('val_loss'))
        self.acc.append(logs.get('mean_iou'))
        self.val_acc.append(logs.get('val_mean_iou'))
        self.i += 1
        print('i=',self.i,'loss=',logs.get('loss'),'val_loss=',logs.get('val_loss'),'mean_iou=',logs.get('mean_iou'),'val_mean_iou=',logs.get('val_mean_iou'))
        
        #choose a random test image and preprocess
        path = np.random.choice(test_files)
        raw = Image.open(f'images/{path}')
        raw = np.array(raw.resize((256, 256)))/255.
        raw = raw[:,:,0:3]
        
        #predict the mask 
        pred = model.predict(np.expand_dims(raw, 0))
        
        #mask post-processing 
        msk  = pred.squeeze()
        msk = np.stack((msk,)*3, axis=-1)
        msk[msk >= 0.5] = 1 
        msk[msk < 0.5] = 0 
        
        #show the mask and the segmented image 
        combined = np.concatenate([raw, msk, raw* msk], axis = 1)
        plt.axis('off')
        plt.imshow(combined)
        plt.show()

train_steps = len(train_files) //batch_size
test_steps = len(test_files) //batch_size
model.fit(train_generator, 
    epochs = 30, steps_per_epoch = train_steps,validation_data = test_generator, validation_steps = test_steps,
    callbacks = build_callbacks(), verbose = 0)

`--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-155-48f0fcd3cdbe> in <module>() 3 model.fit(train_generator, 4 epochs = 30, steps_per_epoch = train_steps,validation_data = test_generator, validation_steps = test_steps, ----> 5 callbacks = build_callbacks(), verbose = 0)

1 frames /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds) 945 # In this case we have created variables on the first call, so we run the 946 # defunned version which is guaranteed to never create variables. --> 947 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable 948 elif self._stateful_fn is not None: 949 # Release the lock early so that multiple threads can perform the call

TypeError: 'NoneType' object is not callable`