class_docstr: CoaT class. 
class_docstr_tok: ["CoaT","class","."]
code: def forward_head(
      self,
      x_feat: Union[torch.Tensor, List[torch.Tensor]],
      pre_logits: bool = False,
  ):
      if isinstance(x_feat, list):
          assert self.aggregate is not None
          if self.global_pool == "avg":
              x = torch.cat(
                  [
                      xl[:, 1:].mean(dim=1, keepdim=True)
                      for xl in x_feat
                  ],
                  dim=1,
              )  # [B, 3, C]
          else:
              x = torch.stack(
                  [xl[:, 0] for xl in x_feat], dim=1
              )  # [B, 3, C]
          x = self.aggregate(x).squeeze(dim=1)  # Shape: [B, C]
      else:
          x = (
              x_feat[:, 1:].mean(dim=1)
              if self.global_pool == "avg"
              else x_feat[:, 0]
          )
      x = self.head_drop(x)
      return x if pre_logits else self.head(x)
  
imports: from functools import partial
  from typing import Tuple, List, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert, LayerNorm
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 9 for calling the CoaT obj's underlying member method code with example usage: obj.forward_head(x_feat, pre_logits) and returns: unknown