def compute_losses(self, x: np.ndarray, y: list[dict[str, np.ndarray | "torch.Tensor"]]) -> dict[str, np.ndarray]:
        """
        Compute losses.

        :param x: Samples of shape (nb_samples, nb_frames, height, width, nb_channels).
        :param y: Target values of format `list[dict[str, np.ndarray]]`, one dictionary for each input image. The keys
                  of the dictionary are:

                  - boxes [N_FRAMES, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                                         0 <= y1 < y2 <= H.
        :return: Dictionary of loss components.
        """
        output = self.compute_loss(x=x, y=y)
        output_dict = {}
        output_dict["torch.nn.L1Loss"] = output
        return output_dict

        Compute losses.

        :param x: Samples of shape (nb_samples, nb_frames, height, width, nb_channels).
        :param y: Target values of format `list[dict[str, np.ndarray]]`, one dictionary for each input image. The keys
                  of the dictionary are:

                  - boxes [N_FRAMES, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                                         0 <= y1 < y2 <= H.
        :return: Dictionary of loss components.
        
art/estimators/object_tracking/pytorch_goturn.py