class_docstr: VisualPrompt
  
  Args:
      - sim_head (`int`):
          Sim head type, must be in ['meanP', 'LSTM', 'Transf', 'Conv_1D', 'Transf_cls'].
      - clip_state_dict (`int`):
          Clip model state dict.
      - num_frames (`int`):
          Number of frame.
class_docstr_tok: ["VisualPrompt","Args",":","-","sim_head","(","`","int","`",")",":","Sim","head","type",",","must","be","in","[","'meanP","'",",","'LSTM","'",",","'Transf","'",",","'Conv_1D","'",",","'Transf_cls","'","]",".","-","clip_state_dict","(","`","int","`",")",":","Clip","model","state","dict",".","-","num_frames","(","`","int","`",")",":","Number","of","frame","."]
code: def forward(self, x):
      _, t, c = x.size()
      x = x.contiguous()
      if self.sim_header == "meanP":
          pass
      elif self.sim_header == "Conv_1D":
          x_original = x
          x = x.view(-1, c, t)
          x = self.shift(x.float())
          x = x.permute(0, 2, 1)
          x = x.type(x_original.dtype) + x_original
  
      elif self.sim_header == "Transf":
          x_original = x
          seq_length = t
          position_ids = torch.arange(
              seq_length, dtype=torch.long, device=x.device
          )
          position_ids = position_ids.unsqueeze(0).expand(x.size(0), -1)
          frame_position_embeddings = self.frame_position_embeddings(
              position_ids
          )
          x = x + frame_position_embeddings
  
          x = x.permute(1, 0, 2)  # NLD -> LND
          x = self.transformer(x)
          x = x.permute(1, 0, 2)  # LND -> NLD
          x = x.type(x_original.dtype) + x_original
  
      elif self.sim_header == "LSTM":
          x_original = x
          x, _ = self.lstm_visual(x.float())
          self.lstm_visual.flatten_parameters()
          x = torch.cat(
              (x, x_original[:, x.size(1) :, ...].contiguous()), dim=1
          )
          x = x.type(x_original.dtype) + x_original
      elif self.sim_header == "Transf_cls":
          x_original = x
          return self.transformer(x).type(x_original.dtype)
  
      else:
          raise ValueError(f"Unknown optimizer: {self.sim_header}")
      return x.mean(dim=1, keepdim=False)
  
imports: import torch
  from torch import nn
  from collections import OrderedDict
  from towhee.models.utils.weight_init import trunc_normal_
  from towhee.models.utils.init_vit_weights import init_vit_weights
label_desc: Use image node_id 2 for calling the VisualPrompt obj's underlying member method code with example usage: obj.forward(x) and returns: x, self