code: def forward_features(self, x):
      if self.grad_checkpointing and not torch.jit.is_scripting():
          x = self.body.s2d(x)
          x = self.body.conv1(x)
          x = checkpoint_seq(
              [
                  self.body.layer1,
                  self.body.layer2,
                  self.body.layer3,
                  self.body.layer4,
              ],
              x,
              flatten=True,
          )
      else:
          x = self.body(x)
      return x
  
imports: from collections import OrderedDict
  from functools import partial
  import torch
  import torch.nn
  from timm.layers import SpaceToDepth, BlurPool2d, ClassifierHead, SEModule, ConvNormActAa, ConvNormAct, DropPath
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 7 for calling the TResNet obj's underlying member method code with example usage: obj.forward_features(x) and returns: x