import tensorflow as tf

        fields.InputDataFields.groundtruth_boxes:
            tf.constant(
                np.array([[0, 0, 1, 1], [.2, .2, 4, 4], [.5, .5, 1, 1]],
                         np.float32)),
        fields.InputDataFields.groundtruth_area:
            tf.constant(np.array([.5, .4, .3])),
        fields.InputDataFields.groundtruth_classes:
            tf.constant(np.array([3, -1, 1], np.int32)),
        fields.InputDataFields.groundtruth_keypoints:
            tf.constant(
                np.array([[[.1, .1]], [[.2, .2]], [[.5, .5]]],
                         np.float32)),
        fields.InputDataFields.groundtruth_keypoint_visibilities:
            tf.constant([True, False, True]),
        fields.InputDataFields.groundtruth_instance_masks:
            tf.constant(np.random.rand(3, 4, 4).astype(np.float32)),
        fields.InputDataFields.groundtruth_is_crowd:
            tf.constant([False, True, False]),
        fields.InputDataFields.groundtruth_difficult:
            tf.constant(np.array([0, 0, 1], np.int32))
    }

    num_classes = 3
    input_transformation_fn = functools.partial(
        inputs.transform_input_data,
        model_preprocess_fn=_fake_model_preprocessor_fn,
