//  [python]
def _create_box_loss(self) -> "Tensor":
        """
        Create the partial loss tensor of this attack from box losses.

        :return: Attack partial loss tensor.
        """
        import tensorflow as tf

        # Get default graph
        default_graph = tf.get_default_graph()

        # Compute box losses
        target_class_phd = tf.placeholder(dtype=tf.int32, shape=[], name="target_class_phd")
        victim_class_phd = tf.placeholder(dtype=tf.int32, shape=[], name="victim_class_phd")
        box_iou_threshold = tf.placeholder(dtype=tf.float32, shape=[], name="box_iou_threshold")

        # Ignore background class
        class_predictions_with_background = self.estimator.predictions["class_predictions_with_background"]
        class_predictions_with_background = class_predictions_with_background[:, 1:]

        # Convert to 1-hot
        # pylint: disable=E1120
        target_class_one_hot = tf.one_hot([target_class_phd - 1], class_predictions_with_background.shape[-1])
        victim_class_one_hot = tf.one_hot([victim_class_phd - 1], class_predictions_with_background.shape[-1])

        box_iou_tensor = default_graph.get_tensor_by_name("Loss/BoxClassifierLoss/Compare/IOU/Select:0")
        box_iou_tensor = tf.reshape(box_iou_tensor, (-1,))
        box_target = tf.to_float(tf.greater_equal(box_iou_tensor, box_iou_threshold))  # pylint: disable=E1123

        # Compute box target loss
        box_target_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_target_weight")

        box_target_logit = class_predictions_with_background[:, target_class_phd - 1]
        box_target_loss = box_target_logit * box_target
        box_target_loss = -1 * tf.reduce_sum(box_target_loss)
        weight_box_target_loss = tf.multiply(x=box_target_loss, y=box_target_weight, name="weight_box_target_loss")

        # Compute box victim loss
        box_victim_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_victim_weight")

        box_victim_logit = class_predictions_with_background[:, victim_class_phd - 1]
        box_victim_loss = box_victim_logit * box_target
        box_victim_loss = tf.reduce_sum(box_victim_loss)
        weight_box_victim_loss = tf.multiply(x=box_victim_loss, y=box_victim_weight, name="weight_box_victim_loss")

        # Compute box target CW loss
        box_target_cw_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_target_cw_weight")
        box_target_cw_confidence = tf.placeholder(dtype=tf.float32, shape=[], name="box_target_cw_confidence")

        box_nontarget_logit = tf.reduce_max(
            class_predictions_with_background * (1 - target_class_one_hot) - 10000 * target_class_one_hot, axis=-1
        )
        box_target_cw_loss = tf.nn.relu(box_nontarget_logit - box_target_logit + box_target_cw_confidence)
        box_target_cw_loss = box_target_cw_loss * box_target
        box_target_cw_loss = tf.reduce_sum(box_target_cw_loss)
        weight_box_target_cw_loss = tf.multiply(
            x=box_target_cw_loss, y=box_target_cw_weight, name="weight_box_target_cw_loss"
        )

        # Compute box victim CW loss
        box_victim_cw_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_victim_cw_weight")
        box_victim_cw_confidence = tf.placeholder(dtype=tf.float32, shape=[], name="box_victim_cw_confidence")

        box_nonvictim_logit = tf.reduce_max(
            class_predictions_with_background * (1 - victim_class_one_hot) - 10000 * victim_class_one_hot, axis=-1
        )
        box_victim_cw_loss = tf.nn.relu(box_victim_logit - box_nonvictim_logit + box_victim_cw_confidence)
        box_victim_cw_loss = box_victim_cw_loss * box_target
        box_victim_cw_loss = tf.reduce_sum(box_victim_cw_loss)
        weight_box_victim_cw_loss = tf.multiply(
            x=box_victim_cw_loss, y=box_victim_cw_weight, name="weight_box_victim_cw_loss"
        )

        # Compute partial loss
        partial_loss = tf.add_n(
            [weight_box_target_loss, weight_box_victim_loss, weight_box_target_cw_loss, weight_box_victim_cw_loss],
            name="partial_box_loss",
        )

        return partial_loss