code: def forward_flex(self, x):
      b, c, h, w = x.shape
  
      pos_embed = self._resize_pos_embed(
          self.pos_embed,
          h // self.patch_size[1],
          w // self.patch_size[0],
      )
  
      B = x.shape[0]
  
      if hasattr(self.patch_embed, "backbone"):
          x = self.patch_embed.backbone(x)
          if isinstance(x, (list, tuple)):
              x = x[
                  -1
              ]  # last feature if backbone outputs list/tuple of features
  
      x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
  
      if getattr(self, "dist_token", None) is not None:
          cls_tokens = self.cls_token.expand(
              B, -1, -1
          )  # stole cls_tokens impl from Phil Wang, thanks
          dist_token = self.dist_token.expand(B, -1, -1)
          x = torch.cat((cls_tokens, dist_token, x), dim=1)
      else:
          cls_tokens = self.cls_token.expand(
              B, -1, -1
          )  # stole cls_tokens impl from Phil Wang, thanks
          x = torch.cat((cls_tokens, x), dim=1)
  
      x = x + pos_embed
      x = self.pos_drop(x)
  
      for blk in self.blocks:
          x = blk(x)
  
      x = self.norm(x)
  
      return x
  
imports: import torch
  import torch.nn
  import timm
  import types
  import math
  import torch.nn.functional
label_desc: Use image node_id 3 for calling a global function with example usage: forward_flex(x) and returns: x