old_contents: import os
  import pytest
  import shutil
  import numpy as np
  np.set_printoptions(threshold=np.inf)
  
  from matchzoo import datapack
  from matchzoo import generators
  from matchzoo import preprocessor
  from matchzoo import models
  from matchzoo import engine
  
  
  def prepare_data():
      """Prepare train & test data."""
      train = []
      test = []
      path = os.path.dirname(__file__)
      with open(os.path.join(path, 'train.txt')) as f:
          train = [tuple(map(str, i.split('\t'))) for i in f]
      with open(os.path.join(path, 'test.txt')) as f:
          test = [tuple(map(str, i.split('\t'))) for i in f]
      return train, test
  
  
  def inte_test_dssm():
      """Test DSSM model."""
      # load data.
      train, test = prepare_data()
      # do pre-processing.
      dssm_preprocessor = preprocessor.DSSMPreprocessor()
      processed_train = dssm_preprocessor.fit_transform(train, stage='train')
      # the dimension of dssm model is the length of tri-letters.
      dim_triletter = processed_train.context['dim_triletter']
      # generator.
      generator = generators.PointGenerator(processed_train)
      X, y = generator[0]
      # Create a dssm model
      dssm_model = models.DSSMModel()
      dssm_model.params['input_shapes'] = [(dim_triletter, ), (dim_triletter, )]
      dssm_model.guess_and_fill_missing_params()
      dssm_model.build()
      dssm_model.compile()
      dssm_model.fit([X['text_left'], X['text_right']], y)
      dssm_model.save('.tmpdir')
  
      # testing
      processed_test = dssm_preprocessor.fit_transform(test, stage='test')
      generator = generators.PointGenerator(processed_test)
      X, y = generator[0]
      dssm_model = engine.load_model('.tmpdir')
      predictions = dssm_model.predict([X['text_left'], X['text_right']])
      print(predictions)
      shutil.rmtree('.tmpdir')
  
      
  
  
  inte_test_dssm()
  
new_contents: import os
  import pytest
  import shutil
  import numpy as np
  np.set_printoptions(threshold=np.inf)
  
  from matchzoo import datapack
  from matchzoo import generators
  from matchzoo import preprocessor
  from matchzoo import models
  from matchzoo import engine
  
  
  def prepare_data():
      """Prepare train & test data."""
      train = []
      test = []
      path = os.path.dirname(__file__)
      with open(os.path.join(path, 'train.txt')) as f:
          train = [tuple(map(str, i.split('\t'))) for i in f]
      with open(os.path.join(path, 'test.txt')) as f:
          test = [tuple(map(str, i.split('\t'))) for i in f]
      return train, test
  
  
  def inte_test_dssm():
      """Test DSSM model."""
      # load data.
      train, test = prepare_data()
      # do pre-processing.
      dssm_preprocessor = preprocessor.DSSMPreprocessor()
      processed_train = dssm_preprocessor.fit_transform(train, stage='train')
      # the dimension of dssm model is the length of tri-letters.
      dim_triletter = processed_train.context['dim_triletter']
      # generator.
      generator = generators.PointGenerator(processed_train)
      X, y = generator[0]
      # Create a dssm model
      dssm_model = models.DSSMModel()
      dssm_model.params['input_shapes'] = [(dim_triletter, ), (dim_triletter, )]
      dssm_model.guess_and_fill_missing_params()
      dssm_model.build()
      dssm_model.compile()
      dssm_model.fit([X.text_left, X.text_right], y)
      dssm_model.save('.tmpdir')
  
      # testing
      processed_test = dssm_preprocessor.fit_transform(test, stage='test')
      generator = generators.PointGenerator(processed_test)
      X, y = generator[0]
      dssm_model = engine.load_model('.tmpdir')
      predictions = dssm_model.predict([X.text_left, X.text_right])
      assert len(predictions) > 0
      assert type(predictions[0][0]) == np.float32
      shutil.rmtree('.tmpdir')
  
      
  
  
  inte_test_dssm()
  
subject: add insertation