code: def vgg(
      x, blocks, is_training, classes, stem, scope=None, reuse=None
  ):
      x = _stack(x, 64, blocks[0], scope="conv1")
      x = _stack(x, 128, blocks[1], scope="conv2")
      x = _stack(x, 256, blocks[2], scope="conv3")
      x = _stack(x, 512, blocks[3], scope="conv4")
      x = _stack(x, 512, blocks[4], scope="conv5")
      if stem:
          return x
      x = flatten(x)
      x = fc(x, 4096, scope="fc6")
      x = relu(x, name="relu6")
      x = dropout(x, keep_prob=0.5, scope="drop6")
      x = fc(x, 4096, scope="fc7")
      x = relu(x, name="relu7")
      x = dropout(x, keep_prob=0.5, scope="drop7")
      x = fc(x, classes, scope="logits")
      x = softmax(x, name="probs")
      return x
  
file_path: tensornets/tensornets/vggs.py
imports: from __future__ import absolute_import
  from __future__ import division
  import tensorflow
  from .layers import conv2d
  from .layers import dropout
  from .layers import flatten
  from .layers import fc
  from .layers import max_pool2d
  from .layers import convrelu
  from .ops import *
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 3 for calling a global function with example usage: vgg(x, blocks, is_training, classes, stem, scope, reuse) and returns: x, x