code: def forward_features(self, x):
      x = self.stem(x)
      if self.grad_checkpointing and not torch.jit.is_scripting():
          x = checkpoint_seq(self.blocks, x)
      else:
          x = self.blocks(x)
      x = self.norm(x)
      return x
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, GluMlp, GatedMlp, DropPath, lecun_normal_, to_2tuple
  from ._builder import build_model_with_cfg
  from ._manipulate import named_apply, checkpoint_seq
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
label_desc: Use image node_id 7 for calling the MlpMixer obj's underlying member method code with example usage: obj.forward_features(x) and returns: x