to 1):
            Relative weight of the classification error in the Hungarian matching cost.
        bbox_cost (`float`, *optional*, defaults to 5):
            Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
        giou_cost (`float`, *optional*, defaults to 2):
            Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
        mask_loss_coefficient (`float`, *optional*, defaults to 1):
            Relative weight of the Focal loss in the panoptic segmentation loss.
        dice_loss_coefficient (`float`, *optional*, defaults to 1):
            Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
        bbox_loss_coefficient (`float`, *optional*, defaults to 5):
            Relative weight of the L1 bounding box loss in the object detection loss.
        giou_loss_coefficient (`float`, *optional*, defaults to 2):
            Relative weight of the generalized IoU loss in the object detection loss.
        eos_coefficient (`float`, *optional*, defaults to 0.1):
            Relative classification weight of the 'no-object' class in the object detection loss.
        num_feature_levels (`int`, *optional*, defaults to 5):
            The number of input feature levels.
        encoder_n_points (`int`, *optional*, defaults to 4):
            The number of sampled keys in each feature level for each attention head in the encoder.
        decoder_n_points (`int`, *optional*, defaults to 4):
            The number of sampled keys in each feature level for each attention head in the decoder.
        two_stage (`bool`, *optional*, defaults to `True`):
            Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
            DETA, which are further fed into the decoder for iterative bounding box refinement.
        two_stage_num_proposals (`int`, *optional*, defaults to 300):
            The number of region proposals to be generated, in case `two_stage` is set to `True`.
        with_box_refine (`bool`, *optional*, defaults to `True`):
            Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes
            based on the predictions from the previous layer.
        focal_alpha (`float`, *optional*, defaults to 0.25):
            Alpha parameter in the focal loss.

    Examples:

    ```python
    >>> from transformers import DetaConfig, DetaModel

    >>> # Initializing a DETA SenseTime/deformable-detr style configuration
    >>> configuration = DetaConfig()

    >>> # Initializing a model (with random weights) from the SenseTime/deformable-detr style configuration
    >>> model = DetaModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Z