code: def test_keras_reader(datadir):
      nn = keras.models.load_model(
          datadir.file("keras_linear_131"), compile=False
      )
      net = load_keras_sequential(nn)
  
      layers = list(net.layers)
      assert len(layers) == 3
      for layer in layers:
          assert layer.activation == "linear"
      assert layers[1].weights.shape == (1, 3)
      assert layers[2].weights.shape == (3, 1)
  
      nn = keras.models.load_model(
          datadir.file("keras_linear_131_sigmoid"), compile=False
      )
      net = load_keras_sequential(nn)
      layers = list(net.layers)
      assert len(layers) == 3
      assert layers[1].activation == "sigmoid"
      assert layers[2].activation == "linear"
      assert layers[1].weights.shape == (1, 3)
      assert layers[2].weights.shape == (3, 1)
  
      nn = keras.models.load_model(
          datadir.file("keras_linear_131_sigmoid_output_activation"),
          compile=False,
      )
      net = load_keras_sequential(nn)
      layers = list(net.layers)
      assert len(layers) == 3
      assert layers[1].activation == "sigmoid"
      assert layers[2].activation == "sigmoid"
      assert layers[1].weights.shape == (1, 3)
      assert layers[2].weights.shape == (3, 1)
  
      nn = keras.models.load_model(datadir.file("big"), compile=False)
      net = load_keras_sequential(nn)
      layers = list(net.layers)
      assert len(layers) == 5
      assert layers[1].activation == "sigmoid"
      assert layers[2].activation == "sigmoid"
      assert layers[3].activation == "sigmoid"
      assert layers[4].activation == "softplus"
      assert layers[1].weights.shape == (1, 100)
      assert layers[2].weights.shape == (100, 100)
      assert layers[3].weights.shape == (100, 100)
      assert layers[4].weights.shape == (100, 1)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_keras_reader(datadir) without return types