如何在tensorflow中访问回调中的训练和测试数据？

import tensorflow as tf

class CustomCallback(tf.keras.callbacks.Callback):
    def on_train_begin(self, logs=None):
        train_data = self.model.train_data  # 访问训练数据
        print("训练数据:", train_data)
    
    def on_train_end(self, logs=None):
        train_data = self.model.train_data  # 访问训练数据
        print("训练数据:", train_data)
    
    def on_test_begin(self, logs=None):
        test_data = self.model.test_data  # 访问测试数据
        print("测试数据:", test_data)
    
    def on_test_end(self, logs=None):
        test_data = self.model.test_data  # 访问测试数据
        print("测试数据:", test_data)
    
    def on_epoch_begin(self, epoch, logs=None):
        train_data = self.model.train_data  # 访问训练数据
        test_data = self.model.test_data  # 访问测试数据
        print("训练数据:", train_data)
        print("测试数据:", test_data)
    
    def on_epoch_end(self, epoch, logs=None):
        train_data = self.model.train_data  # 访问训练数据
        test_data = self.model.test_data  # 访问测试数据
        print("训练数据:", train_data)
        print("测试数据:", test_data)

# 创建模型和数据
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(64, activation='relu', input_dim=10))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
train_data = ...
test_data = ...

# 设置回调函数并开始训练
callback = CustomCallback()
model.fit(train_data, train_labels, epochs=10, validation_data=(test_data, test_labels), callbacks=[callback])