code: def test_luong_not_normalized():
      set_random_state_for_tf_and_np()
      policy = tf.keras.mixed_precision.global_policy()
      create_attention_mechanism = wrapper.LuongAttention
  
      expected_final_output = basic_decoder.BasicDecoderOutput(
          rnn_output=ResultSummary(
              shape=(5, 3, 6),
              dtype=policy.compute_dtype,
              mean=-0.06124732,
          ),
          sample_id=ResultSummary(
              shape=(5, 3), dtype=np.dtype("int32"), mean=2.73333333
          ),
      )
      expected_final_state = wrapper.AttentionWrapperState(
          cell_state=[
              ResultSummary(
                  shape=(5, 9),
                  dtype=policy.compute_dtype,
                  mean=0.52021580,
              ),
              ResultSummary(
                  shape=(5, 9),
                  dtype=policy.compute_dtype,
                  mean=1.0964939,
              ),
          ],
          attention=ResultSummary(
              shape=(5, 6), dtype=policy.compute_dtype, mean=-0.0318060
          ),
          alignments=ResultSummary(
              shape=(5, 8), dtype=policy.compute_dtype, mean=0.125
          ),
          attention_state=ResultSummary(
              shape=(5, 8), dtype=policy.compute_dtype, mean=0.125
          ),
          alignment_history=(),
      )
  
      _test_with_attention(
          create_attention_mechanism,
          expected_final_output,
          expected_final_state,
          attention_mechanism_depth=9,
      )
  
imports: import collections
  from packaging.version import Version
  import pytest
  import numpy
  import tensorflow
  from tensorflow_addons.seq2seq import attention_wrapper
  from tensorflow_addons.seq2seq import basic_decoder
  from tensorflow_addons.seq2seq import sampler
label_desc: Use image node_id 18 for calling a global function with example usage: test_luong_not_normalized() without return types