code: def forward_features(self, x):
      x = self.conv2d_1a(x)
      x = self.conv2d_2a(x)
      x = self.conv2d_2b(x)
      x = self.maxpool_3a(x)
      x = self.conv2d_3b(x)
      x = self.conv2d_4a(x)
      x = self.maxpool_5a(x)
      x = self.mixed_5b(x)
      x = self.repeat(x)
      x = self.mixed_6a(x)
      x = self.repeat_1(x)
      x = self.mixed_7a(x)
      x = self.repeat_2(x)
      x = self.block8(x)
      x = self.conv2d_7b(x)
      return x
  
imports: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import create_classifier, ConvNormAct
  from ._builder import build_model_with_cfg
  from ._manipulate import flatten_modules
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 6 for calling the InceptionResnetV2 obj's underlying member method code with example usage: obj.forward_features(x) and returns: x