使用静态rnn获取TypeError:无法将值None转换为TensorFlow DType

import tensorflow as tf

# 定义输入占位符
input_data = tf.placeholder(tf.float32, [None, None, input_dim], name='input_data')
sequence_length = tf.placeholder(tf.int32, [None], name='sequence_length')

# 定义RNN单元
rnn_cell = tf.nn.rnn_cell.BasicRNNCell(num_units)

# 使用静态RNN
outputs, final_state = tf.nn.static_rnn(rnn_cell, tf.unstack(input_data, axis=1), sequence_length, dtype=tf.float32)

# 确保输入数据不为None
with tf.Session() as sess:
    # 假设我们有一些输入数据
    input_data_value = ...  # 确保这里的数据不为None
    sequence_length_value = ...  # 确保这里的数据不为None
    
    # 运行会话
    sess.run(tf.global_variables_initializer())
    output_values = sess.run(outputs, feed_dict={input_data: input_data_value, sequence_length: sequence_length_value})