code: def forward_features(self, x):
      B = paddle.shape(x)[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, self.feat_channels[0]]).transpose(
          [0, 3, 1, 2]
      )
      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, self.feat_channels[1]]).transpose(
          [0, 3, 1, 2]
      )
      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, self.feat_channels[2]]).transpose(
          [0, 3, 1, 2]
      )
      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, self.feat_channels[3]]).transpose(
          [0, 3, 1, 2]
      )
      outs.append(x)
  
      return outs
  
imports: import math
  from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddle.nn.initializer
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
  from paddleseg.models.backbones.transformer_utils import *
label_desc: Use image node_id 8 for calling the MixVisionTransformer obj's underlying member method code with example usage: obj.forward_features(x) and returns: outs