Hopefully fixed yapf formatting

    if in_layers is None:

      in_layers = self.in_layers

    in_layers = convert_to_layers(in_layers)

    self.build()

    atom_number = in_layers[0].out_tensor

    atom_number = tf.cast(atom_number, dtype=tf.int32)

    atom_features = tf.nn.embedding_lookup(self.embedding_list, atom_number)

    tensors = dict()

    for layer, column in zip(self.features, feature_columns):

      tensors[layer] = tf.feature_column.input_layer(features, [column])

    if weight_column is not None:

      tensors[self.task_weights[0]] = tf.feature_column.input_layer(

          features, [weight_column])

  def test_textcnn_classification(self):

    """Test creating an Estimator from TextCNN for classification."""

    n_tasks = 1

    n_samples = 5

  def test_textcnn_regression(self):

    """Test creating an Estimator from TextCNN for regression."""

    n_tasks = 1

    n_samples = 10

          batch_size=n_samples, epochs=epochs).get_next()

      return {'x': x, 'weights': weights}, y

    print(next(dataset.itersamples()))

    # Create an estimator from it.

    x_col = tf.feature_column.numeric_column('x', shape=(seq_length,))

    weight_col = tf.feature_column.numeric_column('weights', shape=(n_tasks,))