class_docstr: Eva Vision Transformer w/ Abs & Rotary Pos Embed
  
  This class implements the EVA and EVA02 models that were based on the BEiT ViT variant
    * EVA - abs pos embed, global avg pool
    * EVA02 - abs + rope pos embed, global avg pool, SwiGLU, scale Norm in MLP (ala normformer)
class_docstr_tok: ["Eva","Vision","Transformer","w\/","Abs","&","Rotary","Pos","Embed","This","class","implements","the","EVA","and","EVA02","models","that","were","based","on","the","BEiT","ViT","variant","*","EVA","-","abs","pos","embed",",","global","avg","pool","*","EVA02","-","abs","+","rope","pos","embed",",","global","avg","pool",",","SwiGLU",",","scale","Norm","in","MLP","(","ala","normformer",")"]
code: def group_matcher(self, coarse=False):
      matcher = dict(
          stem=r"^cls_token|pos_embed|patch_embed",  # stem and embed
          blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
      )
      return matcher
  
imports: import math
  from typing import Callable, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.checkpoint import checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
  from timm.layers import PatchEmbed, Mlp, GluMlp, SwiGLU, LayerNorm, DropPath, PatchDropout, RotaryEmbeddingCat, apply_rot_embed_cat, apply_keep_indices_nlc, trunc_normal_, resample_patch_embed, resample_abs_pos_embed, to_2tuple, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 6 for calling the Eva obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: matcher