code: def inceptionresnetS(
      x,
      is_training=False,
      classes=1000,
      stem=False,
      scope=None,
      reuse=None,
  ):
      return inceptionresnet(
          x,
          stemS,
          {
              "blocks": 10,
              "filters": [32, 32, [32, 48, 64], 320],
              "reduction": [384, [256, 256, 384]],
          },
          {
              "blocks": 20,
              "filters": [192, [128, 160, 192], 1088],
              "reduction": [256, 384, 288, 320],
          },
          {"blocks": 9, "filters": [192, [192, 224, 256], 2080]},
          is_training,
          classes,
          stem,
          scope,
          reuse,
      )
  
imports: from __future__ import absolute_import
  import tensorflow
  from .layers import avg_pool2d
  from .layers import batch_norm
  from .layers import conv2d
  from .layers import dropout
  from .layers import fc
  from .layers import max_pool2d
  from .layers import sconv2d
  from .layers import convrelu0
  from .layers import convbnrelu
  from .ops import *
  from .utils import pad_info
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 9 for calling a global function with example usage: inceptionresnetS(x, is_training, classes, stem, scope, reuse) and returns: inceptionresnet