defaults to 32):
            The number of relative position bins to be used in the self-attention mechanism.
        max_rel_pos (`int`, *optional*, defaults to 128):
            The maximum number of relative positions to be used in the self-attention mechanism.
        max_rel_2d_pos (`int`, *optional*, defaults to 256):
            The maximum number of relative 2D positions in the self-attention mechanism.
        rel_2d_pos_bins (`int`, *optional*, defaults to 64):
            The number of 2D relative position bins in the self-attention mechanism.
        has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use a spatial attention bias in the self-attention mechanism.
        visual_embed (`bool`, *optional*, defaults to `True`):
            Whether or not to add patch embeddings.
        input_size (`int`, *optional*, defaults to `224`):
            The size (resolution) of the images.
        num_channels (`int`, *optional*, defaults to `3`):
            The number of channels of the images.
        patch_size (`int`, *optional*, defaults to `16`)
            The size (resolution) of the patches.
        classifier_dropout (`float`, *optional*):
            The dropout ratio for the classification head.

    Example:

    ```python
    >>> from transformers import LayoutLMv3Config, LayoutLMv3Model

    >>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration
    >>> configuration = LayoutLMv3Config()

    >>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration
    >>> model = LayoutLMv3Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Z
layoutlmv3éYÄ