code: def darkconv(*args, **kwargs):
      scope = kwargs.pop("scope", None)
      onlyconv = kwargs.pop("onlyconv", False)
      with tf.variable_scope(scope):
          conv_kwargs = {
              "padding": "SAME",
              "activation_fn": None,
              "weights_initializer": variance_scaling_initializer(
                  1.53846
              ),
              "weights_regularizer": l2(5e-4),
              "biases_initializer": None,
              "scope": "conv",
          }
          if onlyconv:
              conv_kwargs.pop("biases_initializer")
          with arg_scope([conv2d], **conv_kwargs):
              x = conv2d(*args, **kwargs)
              if onlyconv:
                  return x
              x = batch_norm(
                  x,
                  decay=0.99,
                  center=False,
                  scale=True,
                  epsilon=1e-5,
                  scope="bn",
              )
              x = bias_add(x, scope="bias")
              x = leaky_relu(x, alpha=0.1, name="lrelu")
              return x
  
imports: from __future__ import absolute_import
  import numpy
  import tensorflow
  from .ops import conv2d_primitive
  from .ops import leaky_relu
  from .ops import relu
  from .ops import relu6
  from .ops import reshape
  from .ops import swish
  from .utils import arg_scope
  from .utils import remove_commons
  from .version_utils import tf_later_than
label_desc: Use image node_id 14 for calling a global function with example usage: darkconv() and returns: x, x