class_docstr: Shunted Transformer.
  
  Args:
      img_size (`int`): image size (width or height)
      in_chans (`int`): input channels
      num_classes (`int`): number of classes for classification
      embed_dims (`List[int]`): a list of embedding dimensions at different stages
      num_heads (`List[int]`): a list of attention heads at different stages
      mlp_ratios (`List[int]`): a list of mlp ratios at different stages
      qkv_bias (`bool`): flag to enable bias for qkv
      qk_scale (`int`): number to scale qk
      drop_rate (`float`): drop rate of mlp layers
      attn_drop_rate (`float`): drop rate of attention
      drop_path_rate (`float`): drop rate of drop path
      norm_layer (`nn.Module`): normalization layer
      depths (`List[int]`): a list of stochastic depths for blocks
      sr_ratios (`List[int]`): a list of shunt rates in blocks at each stage
      num_stages (`int`): number of stages
      num_conv (`int`): number of additional convolutions in patch embedding layer at the first stage
class_docstr_tok: ["Shunted","Transformer",".","Args",":","img_size","(","`","int","`",")",":","image","size","(","width","or","height",")","in_chans","(","`","int","`",")",":","input","channels","num_classes","(","`","int","`",")",":","number","of","classes","for","classification","embed_dims","(","`","List","[","int","]","`",")",":","a","list","of","embedding","dimensions","at","different","stages","num_heads","(","`","List","[","int","]","`",")",":","a","list","of","attention","heads","at","different","stages","mlp_ratios","(","`","List","[","int","]","`",")",":","a","list","of","mlp","ratios","at","different","stages","qkv_bias","(","`","bool","`",")",":","flag","to","enable","bias","for","qkv","qk_scale","(","`","int","`",")",":","number","to","scale","qk","drop_rate","(","`","float","`",")",":","drop","rate","of","mlp","layers","attn_drop_rate","(","`","float","`",")",":","drop","rate","of","attention","drop_path_rate","(","`","float","`",")",":","drop","rate","of","drop","path","norm_layer","(","`","nn.Module","`",")",":","normalization","layer","depths","(","`","List","[","int","]","`",")",":","a","list","of","stochastic","depths","for","blocks","sr_ratios","(","`","List","[","int","]","`",")",":","a","list","of","shunt","rates","in","blocks","at","each","stage","num_stages","(","`","int","`",")",":","number","of","stages","num_conv","(","`","int","`",")",":","number","of","additional","convolutions","in","patch","embedding","layer","at","the","first","stage"]
code: def forward(self, x):
      x = self.forward_features(x)
      x = self.head(x)
      return x
  
file_path: towhee/towhee/models/shunted_transformer/shunted_transformer.py
imports: import torch
  from torch import nn
  from towhee.models.utils import create_model
  from towhee.models.utils.init_vit_weights import init_vit_weights
  from towhee.models.shunted_transformer import OverlapPatchEmbed, HeadPatchEmbed, Block, get_configs
label_desc: Use image node_id 3 for calling the ShuntedTransformer obj's underlying member method code with example usage: obj.forward(x) and returns: x