def METHOD_NAME(depth, width, height):
    """Resize depth map and bring to CPU (numpy).
    Args:
        depth (tensor): depth
        width (int): image width
        height (int): image height
    Returns:
        array: processed depth
    """
    depth = torch.squeeze(depth[0, :, :, :]).to('cpu')
    depth_resized = cv2.resize(
        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC)
    return depth_resized