code: def forward_features(self, x):
      x = self.patch_embed(x)
      if self.grad_checkpointing and not torch.jit.is_scripting():
          x = checkpoint_seq(self.stages, x)
      else:
          x = self.stages(x)
      return x
  
imports: import itertools
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SqueezeExcite, SelectAdaptivePool2d, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 7 for calling the EfficientVitMsra obj's underlying member method code with example usage: obj.forward_features(x) and returns: x