使用BasicLSTMCell无法使用Tensor Object

I have the following code:

我有以下代码：

def dense_layers(pool3):
    with tf.variable_scope('local1') as scope:
        # Move everything into depth so we can perform a single matrix multiply.
        shape_d = pool3.get_shape()
        shape = shape_d[1] * shape_d[2] * shape_d[3]
        # tf_shape = tf.stack(shape)
        tf_shape = 1024

        print("shape:", shape, shape_d[1], shape_d[2], shape_d[3])

        # So note that tf_shape = 1024, this means that we have 1024 features are fed into the network. And
        # the batch size = 1024. Therefore, the aim is to divide the batch_size into num_steps so that
        reshape = tf.reshape(pool3, [-1, tf_shape])
        # Now we need to reshape/divide the batch_size into num_steps so that we would be feeding a sequence
        # And note that most importantly is to have batch_partition_length followed by step_size in the parameter list.
        lstm_inputs = tf.reshape(reshape, [batch_partition_length, step_size, tf_shape])

        # print('RNN inputs shape: ', lstm_inputs.get_shape()) # -> (128, 8, 1024).

        # Note that the state_size is the number of neurons.
        lstm = tf.contrib.rnn.BasicLSTMCell(state_size)
        lstm_outputs, final_state = tf.nn.dynamic_rnn(cell=lstm, inputs=lstm_inputs, initial_state=init_state)
        tf.assign(init_state, final_state)
def den