def add_densepose_head_cse_config(cfg: CN) -> None:
    """
    Add configuration options for Continuous Surface Embeddings (CSE)
    """
    _C = cfg
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE = CN()
    # Dimensionality D of the embedding space
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE = 16
    # Embedder specifications for various mesh IDs
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS = CN(new_allowed=True)
    # normalization coefficient for embedding distances
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_DIST_GAUSS_SIGMA = 0.01
    # normalization coefficient for geodesic distances
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.GEODESIC_DIST_GAUSS_SIGMA = 0.01
    # embedding loss weight
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_WEIGHT = 0.6
    # embedding loss name, currently the following options are supported:
    # - EmbeddingLoss: cross-entropy on vertex labels
    # - SoftEmbeddingLoss: cross-entropy on vertex label combined with
    #    Gaussian penalty on distance between vertices
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_NAME = "EmbeddingLoss"
    # optimizer hyperparameters
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.FEATURES_LR_FACTOR = 1.0
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_LR_FACTOR = 1.0
    # Shape to shape cycle consistency loss parameters:
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False})
    # shape to shape cycle consistency loss weight
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.025
    # norm type used for loss computation
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.NORM_P = 2
    # normalization term for embedding similarity matrices
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.TEMPERATURE = 0.05
    # maximum number of vertices to include into shape to shape cycle loss
    # if negative or zero, all vertices are considered
    # if positive, random subset of vertices of given size is considered
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.MAX_NUM_VERTICES = 4936
    # Pixel to shape cycle consistency loss parameters:
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False})
    # pixel to shape cycle consistency loss weight
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.0001
    # norm type used for loss computation
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NORM_P = 2
    # map images to all meshes and back (if false, use only gt meshes from the batch)
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.USE_ALL_MESHES_NOT_GT_ONLY = False
    # Randomly select at most this number of pixels from every instance
    # if negative or zero, all vertices are considered
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NUM_PIXELS_TO_SAMPLE = 100
    # normalization factor for pixel to pixel distances (higher value = smoother distribution)
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.PIXEL_SIGMA = 5.0
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_PIXEL_TO_VERTEX = 0.05
    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_VERTEX_TO_PIXEL = 0.05

    Add configuration options for Continuous Surface Embeddings (CSE)
    
EVA-02/det/projects/DensePose/densepose/config.py