ax_position_embeddings]`. Use the same value
            as `max_position_embeddings`.
        pad_token_id (`int`, *optional*, defaults to 0):
            The value used to pad input_ids.
        position_biased_input (`bool`, *optional*, defaults to `False`):
            Whether add absolute position embedding to content embedding.
        pos_att_type (`List[str]`, *optional*):
            The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`,
            `["p2c", "c2p"]`, `["p2c", "c2p"]`.
        layer_norm_eps (`float`, optional, defaults to 1e-12):
            The epsilon used by the layer normalization layers.

    Example:

    ```python
    >>> from transformers import DebertaV2Config, DebertaV2Model

    >>> # Initializing a DeBERTa-v2 microsoft/deberta-v2-xlarge style configuration
    >>> configuration = DebertaV2Config()

    >>> # Initializing a model (with random weights) from the microsoft/deberta-v2-xlarge style configuration
    >>> model = DebertaV2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```z
deberta-v2édô