code: def reductionA(x, f, padding="VALID", scope=None):
      if padding == "VALID":
          # Reduction-A (Fig. 7 in the v4 paper)
          conv2 = conv(
              x, f[0], 3, stride=2, padding=padding, scope="3x3"
          )
      else:
          conv2 = conv(x, f[0][0], 1, scope="3x3/r")
          conv2 = conv(conv2, f[0][1], 3, stride=2, scope="3x3/1")
  
      conv3 = conv(x, f[1][0], 1, scope="d3x3/r")
      conv3 = conv(conv3, f[1][1], 3, scope="d3x3/1")
      conv3 = conv(
          conv3, f[1][2], 3, stride=2, padding=padding, scope="d3x3/2"
      )
  
      pool = max_pool2d(x, 3, stride=2, padding=padding)
  
      x = concat([conv2, conv3, pool], axis=3, name="concat")
      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 15 for calling a global function with example usage: reductionA(x, f, padding, scope) and returns: x