class_docstr: Args:
docstr: Args:
      x: input to the model of shape 1 x C x T x H x W
      input_type: Optional[str] one of ["image", "video", "rgbd"]
          if self.multimodal_model iss True
  Returns:
      preds: tensor of shape (1, num_classes)
docstr_tok: ["Args",":","x",":","input","to","the","model","of","shape","1","x","C","x","T","x","H","x","W","input_type",":","Optional","[","str","]","one","of","[","``","image","''",",","``","video","''",",","``","rgbd","''","]","if","self.multimodal_model","iss","True","Returns",":","preds",":","tensor","of","shape","(","1",",","num_classes",")"]
code: def forward(self, x: torch.Tensor, input_type: Optional[str] = None):
      """
      Args:
          x: input to the model of shape 1 x C x T x H x W
          input_type: Optional[str] one of ["image", "video", "rgbd"]
              if self.multimodal_model iss True
      Returns:
          preds: tensor of shape (1, num_classes)
      """
      features = self.forward_features(x)
      return self.head(features, input_type=input_type)
  
imports: from towhee.models.video_swin_transformer.video_swin_transformer import VideoSwinTransformer
  from typing import Any, Optional, Union
  import torch
  from torch import nn
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 4 for calling the OmnivoreModel obj's underlying member method code with example usage: obj.forward(x, input_type) and returns: self