import tensorflow as tf

    self.assertAllEqual(transformed_inputs[fields.InputDataFields.image].dtype,
                        tf.float32)
    self.assertAllEqual(transformed_inputs[fields.InputDataFields.image].shape,
                        [4, 4, 5])
    self.assertAllClose(transformed_inputs[fields.InputDataFields.image],
                        np.concatenate((image, additional_channels), axis=2))

  def test_use_multiclass_scores_when_present(self):
    image = np.random.rand(4, 4, 3).astype(np.float32)
    tensor_dict = {
        fields.InputDataFields.image:
            tf.constant(image),
        fields.InputDataFields.groundtruth_boxes:
            tf.constant(np.array([[.5, .5, 1, 1], [.5, .5, 1, 1]], np.float32)),
        fields.InputDataFields.multiclass_scores:
            tf.constant(np.array([0.2, 0.3, 0.5, 0.1, 0.6, 0.3], np.float32)),
        fields.InputDataFields.groundtruth_classes:
            tf.constant(np.array([1, 2], np.int32))
    }

    input_transformation_fn = functools.partial(
        inputs.transform_input_data,
