code: def forward(self, net: torch.Tensor) -> torch.Tensor:
      net, hw = self.embed(net)
      net = self.encoder(net, hw)
      net = (
          net.view(net.shape[0], *hw, -1)
          .permute(0, 3, 1, 2)
          .contiguous()
      )
      return net
  
file_path: carefree-learn/cflearn/models/cv/encoder/backbone/models/transformer.py
imports: import torch
  from torch import nn
  from typing import Any
  from typing import List
  from typing import Optional
  from cftool.misc import shallow_copy_dict
  from ..register import register_backbone
  from ......modules.blocks import ImgToPatches
  from ......modules.blocks import MixedStackedEncoder
label_desc: Use image node_id 2 for calling the MixViTBlock obj's underlying member method code with example usage: obj.forward(net) and returns: net