code: def pnasnet(
      x,
      stem_filters,
      blocks,
      filters,
      scaling,
      is_training,
      classes,
      stem,
      scope=None,
      reuse=None,
  ):
      x = conv(
          x, stem_filters, 3, stride=2, padding="VALID", scope="conv0"
      )
  
      x, p = normalP(
          x, None, filters * scaling ** (-2), stride=2, scope="stem1"
      )
      x, p = normalP(
          x, p, filters * scaling ** (-1), stride=2, scope="stem2"
      )
  
      for i in range(1, blocks + 1):
          x, p = normalP(x, p, filters, scope="normal%d" % i)
  
      x, p = normalP(
          x,
          p,
          filters * scaling,
          stride=2,
          scope="reduction%d" % blocks,
      )
  
      for i in range(blocks + 1, blocks * 2):
          x, p = normalP(x, p, filters * scaling, scope="normal%d" % i)
  
      x, p = normalP(
          x,
          p,
          filters * scaling**2,
          stride=2,
          scope="reduction%d" % (blocks * 2 - 1),
      )
  
      for i in range(blocks * 2, blocks * 3 - 1):
          x, p = normalP(
              x, p, filters * scaling**2, scope="normal%d" % i
          )
  
      x = relu(x, name="relu")
      if stem:
          return x
      x = reduce_mean(x, [1, 2], name="avgpool")
      x = dropout(x, keep_prob=0.5, scope="dropout")
      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 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 convbn
  from .layers import sconvbn
  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: pnasnet(x, stem_filters, blocks, filters, scaling, is_training, classes, stem, scope, reuse) and returns: x, x