如何在进程之间共享模型参数？有两个过程，一个用于训练模型，另一个用于预测。

import tensorflow as tf
import multiprocessing

# Process used for training of model

def train_run(model_variables):
    model_variables[:] = model.variables 

# Process used for predicting

def predict_run(model_variables):
    print(model_variables[:])

model = tf.keras.applications.MobileNet()
model_variables = multiprocessing.Array(tf.Variable, model.variables)

p1 = multiprocessing.Process(target=train_run, args=(model_variables,))
p2 = multiprocessing.Process(target=predict_run, args=(model_variables,))