class_docstr: Visual Transformer with Prompt. This class add prompts to a visual
  transformer to implement the Method Learning to Prompt (L2P)
  
  Implementation based on VisionTransformer from timm library
class_docstr_tok: ["Visual","Transformer","with","Prompt",".","This","class","add","prompts","to","a","visual","transformer","to","implement","the","Method","Learning","to","Prompt","(","L2P",")","Implementation","based","on","VisionTransformer","from","timm","library"]
code: def forward_head(self, res, pre_logits: bool = False):
      x = res["x"]
      if self.class_token and self.head_type == "token":
          x = x[:, 0]
      elif self.head_type == "gap" and self.global_pool == "avg":
          x = x.mean(dim=1)
      elif self.head_type == "prompt" and self.prompt_pool:
          x = (
              x[:, 1 : (1 + self.total_prompt_len)]
              if self.class_token
              else x[:, 0 : self.total_prompt_len]
          )
          x = x.mean(dim=1)
      elif (
          self.head_type == "token+prompt"
          and self.prompt_pool
          and self.class_token
      ):
          x = x[:, 0 : self.total_prompt_len + 1]
          x = x.mean(dim=1)
      else:
          raise ValueError(f"Invalid classifier={self.classifier}")
  
      res["pre_logits"] = x
  
      x = self.fc_norm(x)
  
      res["logits"] = self.head(x)
  
      return res
  
file_path: avalanche/avalanche/models/timm_vit.py
imports: import math
  import re
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from avalanche.models import Prompt
label_desc: Use image node_id 3 for calling the ViTWithPrompt obj's underlying member method code with example usage: obj.forward_head(res, pre_logits) and returns: res