code: def inception4(
      x,
      is_training=False,
      classes=1000,
      stem=False,
      scope=None,
      reuse=None,
  ):
      x = stemA(x)
      for i in range(4):
          x = inceptionA(
              x,
              [96, [64, 96], [64, 96], 96],
              scope="block5%c" % (98 + i),
          )
      x = reductionA(x, [384, [192, 224, 256]], scope="block6a")
  
      for i in range(7):
          x = inceptionB(
              x,
              [384, [192, 224, 256], [192, 224], 128],
              scope="block6%c" % (98 + i),
          )
      x = reductionB(x, [[192, 192], [256, 320]], scope="block7a")
  
      for i in range(3):
          x = inceptionC(
              x,
              [256, [384, 256, 256], [384, 448, 512, 256], 256],
              scope="block7%c" % (98 + i),
          )
      if stem:
          return x
  
      x = reduce_mean(x, [1, 2], name="avgpool")
      x = dropout(x, keep_prob=0.8, scope="dropout")
      x = fc(x, classes, scope="logits")
      x = softmax(x, name="probs")
      return x
  
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 5 for calling a global function with example usage: inception4(x, is_training, classes, stem, scope, reuse) and returns: x, x