import tensorflow as tf
import numpy as np

tf.set_random_seed(765)
np.random.seed(765)
tf.reset_default_graph()

n_inputs = 3
n_neurons = 5
n_steps = 2

X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
seq_length = tf.placeholder(tf.int32, [None])

basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)
outputs, states = tf.nn.dynamic_rnn(basic_cell, X, sequence_length=seq_length,
    dtype=tf.float32)

init = tf.global_variables_initializer()

X_batch = np.array([
        [[0, 1, 2], [9, 8, 7]], # instance 1
        [[3, 4, 5], [0, 0, 0]], # instance 2
        [[6, 7, 8], [6, 5, 4]], # instance 3
        [[9, 0, 1], [3, 2, 1]], # instance 4
    ])

seq_length_batch = np.array([2,1,2,2])

with tf.Session() as sess:
    init.run()
    outputs_val, states_val = sess.run(
        [outputs, states], feed_dict={X: X_batch, seq_length: seq_length_batch}
    )
    print(outputs_val)
    print(states_val)
Add code for setting sequence length for RNN