code: def add_VGG_CNN_M_1024_conv5_body(model):
      model.Conv("data", "conv1", 3, 96, 7, pad=0, stride=2)
      model.Relu("conv1", "conv1")
      model.LRN(
          "conv1", "norm1", size=5, alpha=0.0005, beta=0.75, bias=2.0
      )
      model.MaxPool("norm1", "pool1", kernel=3, pad=0, stride=2)
      model.StopGradient("pool1", "pool1")
      # No updates at conv1 and below (norm1 and pool1 have no params,
      # so we can stop gradients before them, too)
      model.Conv("pool1", "conv2", 96, 256, 5, pad=0, stride=2)
      model.Relu("conv2", "conv2")
      model.LRN(
          "conv2", "norm2", size=5, alpha=0.0005, beta=0.75, bias=2.0
      )
      model.MaxPool("norm2", "pool2", kernel=3, pad=0, stride=2)
      model.Conv("pool2", "conv3", 256, 512, 3, pad=1, stride=1)
      model.Relu("conv3", "conv3")
      model.Conv("conv3", "conv4", 512, 512, 3, pad=1, stride=1)
      model.Relu("conv4", "conv4")
      model.Conv("conv4", "conv5", 512, 512, 3, pad=1, stride=1)
      blob_out = model.Relu("conv5", "conv5")
      return blob_out, 512, 1.0 / 16.0
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from detectron.core.config import cfg
label_desc: Use image node_id 1 for calling a global function with example usage: add_VGG_CNN_M_1024_conv5_body(model) and returns: blob_out,  int,  unknown