code: def normalP(x, p, filters, stride=1, scope=None):
      filters = int(filters)
      p = adjust(p, x, filters)
  
      h = relu(x)
      h = conv(h, filters, 1, scope="1x1")
  
      x1 = add(
          sconv(p, filters, 5, stride, scope="left1"),
          pool(p, filters, stride, scope="right1"),
          name="add1",
      )
      x2 = add(
          sconv(h, filters, 7, stride, scope="left2"),
          pool(h, filters, stride, scope="right2"),
          name="add2",
      )
      x3 = add(
          sconv(h, filters, 5, stride, scope="left3"),
          sconv(h, filters, 3, stride, scope="right3"),
          name="add3",
      )
      x4 = add(
          sconv(x3, filters, 3, scope="left4"),
          pool(h, filters, stride, scope="right4"),
          name="add4",
      )
      x5 = add(
          sconv(p, filters, 3, stride, scope="left5"),
          conv(relu(h), filters, 1, stride, scope="right5")
          if stride > 1
          else h,
          name="add5",
      )
  
      return concat([x1, x2, x3, x4, x5], 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 12 for calling a global function with example usage: normalP(x, p, filters, stride, scope) and returns: concat,  x