code: def resnet(
      x,
      preact,
      stack_fn,
      is_training,
      classes,
      stem,
      scope=None,
      reuse=None,
  ):
      x = pad(x, pad_info(7), name="conv1/pad")
      if preact:
          x = conv2d(x, 64, 7, stride=2, scope="conv1")
      else:
          x = conv(x, 64, 7, stride=2, scope="conv1")
          x = relu(x, name="conv1/relu")
      x = pad(
          x,
          pad_info(0 if stem else 3, symmetry=not preact),
          "SYMMETRIC",
          name="pool1/pad",
      )
      x = max_pool2d(x, 3, stride=2, scope="pool1")
      x = stack_fn(x)
      if stem:
          return x
      x = reduce_mean(x, [1, 2], name="avgpool")
      x = fc(x, classes, scope="logits")
      x = softmax(x, name="probs")
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  import tensorflow
  from .layers import batch_norm
  from .layers import conv2d
  from .layers import fc
  from .layers import max_pool2d
  from .layers import sconv2d
  from .layers import convbn
  from .layers import gconvbn
  from .ops import *
  from .utils import pad_info
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 2 for calling a global function with example usage: resnet(x, preact, stack_fn, is_training, classes, stem, scope, reuse) and returns: x, x