code: def tinydarknet19(
      x,
      is_training=False,
      classes=1000,
      stem=False,
      scope=None,
      reuse=None,
  ):
      x = conv(x, 16, 3, scope="conv1")
      x = pool(x, 2, stride=2, scope="pool1")
      x = conv(x, 32, 3, scope="conv2")
      x = pool(x, 2, stride=2, scope="pool2")
      x = conv(x, 64, 3, scope="conv3")
      x = pool(x, 2, stride=2, scope="pool3")
      x = conv(x, 128, 3, scope="conv4")
      x = pool(x, 2, stride=2, scope="pool4")
      x = conv(x, 256, 3, scope="conv5")
      x = pool(x, 2, stride=2, scope="pool5")
      x = conv(x, 512, 3, scope="conv6")
      if stem:
          return x
  
      x = reduce_mean(x, [1, 2], name="avgpool")
      x = fc(x, classes, scope="logits")
      x = softmax(x, name="probs")
      return x
  
file_path: tensornets/tensornets/darknets.py
imports: from __future__ import absolute_import
  from __future__ import division
  import tensorflow
  from .layers import batch_norm
  from .layers import bias_add
  from .layers import conv2d
  from .layers import darkconv
  from .layers import fc
  from .layers import max_pool2d
  from .ops import *
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 4 for calling a global function with example usage: tinydarknet19(x, is_training, classes, stem, scope, reuse) and returns: x, x