class_docstr: Tests the UnidirectionalRNNEncoder class.
class_docstr_tok: ["Tests","the","UnidirectionalRNNEncoder","class","."]
code: def test_encode(self):
      inputs = tf.random_normal(
          [self.batch_size, self.sequence_length, self.input_depth]
      )
      example_length = (
          tf.ones(self.batch_size, dtype=tf.int32)
          * self.sequence_length
      )
  
      encode_fn = rnn_encoder.UnidirectionalRNNEncoder(
          self.params, self.mode
      )
      encoder_output = encode_fn(inputs, example_length)
  
      with self.test_session() as sess:
          sess.run(tf.global_variables_initializer())
          encoder_output_ = sess.run(encoder_output)
  
      np.testing.assert_array_equal(
          encoder_output_.outputs.shape,
          [self.batch_size, self.sequence_length, 32],
      )
      self.assertIsInstance(
          encoder_output_.final_state, tf.contrib.rnn.LSTMStateTuple
      )
      np.testing.assert_array_equal(
          encoder_output_.final_state.h.shape, [self.batch_size, 32]
      )
      np.testing.assert_array_equal(
          encoder_output_.final_state.c.shape, [self.batch_size, 32]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import tensorflow
  import numpy
  from seq2seq.encoders import rnn_encoder
label_desc: Use image node_id 2 for calling the UnidirectionalRNNEncoderTest obj's underlying member method code with example usage: obj.test_encode() without return types