code: def forward_features(self, x):
      B = x.shape[0]
      outs = []
  
      # stage 1
      x, H, W = self.patch_embed1(x)
      for i, blk in enumerate(self.block1):
          x = blk(x, H, W)
      x = self.norm1(x)
      x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
      outs.append(x)
  
      # stage 2
      x, H, W = self.patch_embed2(x)
      for i, blk in enumerate(self.block2):
          x = blk(x, H, W)
      x = self.norm2(x)
      x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
      outs.append(x)
  
      # stage 3
      x, H, W = self.patch_embed3(x)
      for i, blk in enumerate(self.block3):
          x = blk(x, H, W)
      x = self.norm3(x)
      x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
      outs.append(x)
  
      # stage 4
      x, H, W = self.patch_embed4(x)
      for i, blk in enumerate(self.block4):
          x = blk(x, H, W)
      x = self.norm4(x)
      x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
      outs.append(x)
  
      return outs
  
imports: import math
  from functools import partial
  from typing import Optional
  import torch
  import torch.nn
  from pytorch_toolbelt.modules.interfaces import AbstractEncoder, FeatureMapsSpecification
  from pytorch_toolbelt.modules.activations import instantiate_activation_block, ACT_GELU
  from pytorch_toolbelt.modules.drop_path import DropPath
label_desc: Use image node_id 6 for calling the MixVisionTransformer obj's underlying member method code with example usage: obj.forward_features(x) and returns: outs