code: def test_bahdanau_normalized_dtype(dtype):
      dummy_data = DummyData2()
      encoder_outputs = dummy_data.encoder_outputs.astype(dtype)
      decoder_inputs = dummy_data.decoder_inputs.astype(dtype)
      attention_mechanism = wrapper.BahdanauAttention(
          units=dummy_data.units,
          memory=encoder_outputs,
          memory_sequence_length=dummy_data.encoder_sequence_length,
          normalize=True,
          dtype=dtype,
      )
      cell = tf.keras.layers.LSTMCell(
          dummy_data.units, recurrent_activation="sigmoid", dtype=dtype
      )
      cell = wrapper.AttentionWrapper(
          cell, attention_mechanism, dtype=dtype
      )
  
      sampler = sampler_py.TrainingSampler()
      my_decoder = basic_decoder.BasicDecoder(
          cell=cell, sampler=sampler, dtype=dtype
      )
  
      final_outputs, final_state, _ = my_decoder(
          decoder_inputs,
          initial_state=cell.get_initial_state(
              batch_size=dummy_data.batch, dtype=dtype
          ),
          sequence_length=dummy_data.decoder_sequence_length,
      )
      assert isinstance(final_outputs, basic_decoder.BasicDecoderOutput)
      assert final_outputs.rnn_output.dtype == dtype
      assert isinstance(final_state, wrapper.AttentionWrapperState)
  
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 13 for calling a global function with example usage: test_bahdanau_normalized_dtype(dtype) without return types