code: def inceptionresnet(
      x,
      stem_fn,
      A,
      B,
      C,
      is_training,
      classes,
      stem,
      scope=None,
      reuse=None,
  ):
      x = stem_fn(x)
      for i in range(A["blocks"]):
          x = inceptionRA(
              x, A["filters"], scale=0.17, scope="block5%c" % (99 + i)
          )
      x = reductionA(x, A["reduction"], scope="block6a")
  
      for i in range(B["blocks"]):
          x = inceptionRB(
              x,
              B["filters"],
              k=7,
              scale=0.1,
              scope="block6%c" % (98 + i),
          )
      x = reductionRB(x, B["reduction"], scope="block7a")
  
      for i in range(C["blocks"]):
          x = inceptionRB(
              x,
              C["filters"],
              k=3,
              scale=0.2,
              scope="block7%c" % (98 + i),
          )
  
      if C["blocks"] == 9:
          x = inceptionRB(
              x,
              C["filters"],
              k=3,
              activation_fn=None,
              scope="block7%c" % (98 + C["blocks"]),
          )
          x = conv(x, 1536, 1, scope="conv")
      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 6 for calling a global function with example usage: inceptionresnet(x, stem_fn, A, B, C, is_training, classes, stem, scope, reuse) and returns: x, x