Zj& dSrSSKJr SSKJr SSKJr \"SS9\"SS \555rS /rg ) zLeViT model configuration)strict)PreTrainedConfig)auto_docstringzfacebook/levit-128S) checkpointc^\rSrSr%SrSrSr\\\-\ \\4-\ S'Sr \\ S'Sr \\ S'S r \\ S 'S r\\ S 'S r\\\-\ \\4-\ S'Sr\\\ \S4-\ S'Sr\\\ \S4-\ S'Sr\\\ \S4-\ S'Sr\\\ \S4-\ S'Sr\\ S'Sr\\\ \S4-\ S'Sr\\\ \S4-\ S'Sr\\ S'U4SjrS rU=r$)! LevitConfigax stride (`int`, *optional*, defaults to 2): The stride size for the initial convolution layers of patch embedding. padding (`int`, *optional*, defaults to 1): The padding size for the initial convolution layers of patch embedding. key_dim (`list[int]`, *optional*, defaults to `[16, 16, 16]`): The size of key in each of the encoder blocks. attention_ratio (`list[int]`, *optional*, defaults to `[2, 2, 2]`): Ratio of the size of the output dimension compared to input dimension of attention layers. Example: ```python >>> from transformers import LevitConfig, LevitModel >>> # Initializing a LeViT levit-128S style configuration >>> configuration = LevitConfig() >>> # Initializing a model (with random weights) from the levit-128S style configuration >>> model = LevitModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```levit image_sizer num_channels kernel_sizestridepadding patch_size)i. hidden_sizes) num_attention_heads)rrrdepths)rrrkey_dimrdrop_path_rate)rrr mlp_ratioattention_ratiog{Gz?initializer_rangec >SURSURSURS-SSS/SURSURSURS-SSS//Ul[TU]"S0UD6 g)N Subsamplerrrr)rrdown_opssuper __post_init__)selfkwargs __class__s ~/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/levit/configuration_levit.pyr(LevitConfig.__post_init__Cs $,,q/4+<+r@sG .3#010("0(20(f /r.