code: def normalA(x, p, filters, scope=None):
      """Normal cell for NASNet-A (Fig. 4 in the paper)"""
      p = adjust(p, x, filters)
  
      h = relu(x)
      h = conv(h, filters, 1, scope="1x1")
  
      x1 = add(sconv(h, filters, 3, scope="left1"), h, name="add1")
      x2 = add(
          sconv(p, filters, 3, scope="left2"),
          sconv(h, filters, 5, scope="right2"),
          name="add2",
      )
      x3 = add(
          avg_pool2d(h, 3, stride=1, scope="left3"), p, name="add3"
      )
      x4 = add(
          avg_pool2d(p, 3, stride=1, scope="left4"),
          avg_pool2d(p, 3, stride=1, scope="right4"),
          name="add4",
      )
      x5 = add(
          sconv(p, filters, 5, scope="left5"),
          sconv(p, filters, 3, scope="right5"),
          name="add5",
      )
  
      return concat([p, x2, x5, x3, x4, x1], axis=3, name="concat"), 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 9 for calling a global function with example usage: normalA(x, p, filters, scope) and returns: concat,  x