如何通过model.fit()提取LSTM模型的细胞状态？

# 导入所需的库和模块
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import LSTM, Input

# 定义原始的LSTM模型
input_shape = (10, 1)  # 输入数据的形状
lstm_units = 64  # LSTM单元的数量

input_layer = Input(shape=input_shape)
lstm_layer = LSTM(units=lstm_units)(input_layer)
output_layer = Dense(1)(lstm_layer)

lstm_model = Model(inputs=input_layer, outputs=output_layer)

# 加载原始LSTM模型的权重
lstm_model.load_weights('lstm_model_weights.h5')

# 定义新的模型，只包含LSTM模型的细胞状态
lstm_state_model = Model(inputs=lstm_model.input,
                         outputs=lstm_model.layers[1].output)

# 使用model.predict()方法获取LSTM模型的细胞状态
input_data = ...  # 输入数据
lstm_state = lstm_state_model.predict(input_data)

# 打印LSTM模型的细胞状态
print(lstm_state)