code: def yolo3_mobilenet0_25_custom(
      classes,
      transfer=None,
      pretrained_base=True,
      pretrained=False,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      """YOLO3 multi-scale with mobilenet0.25 base network on custom dataset.
      Parameters
      ----------
      classes : iterable of str
          Names of custom foreground classes. `len(classes)` is the number of foreground classes.
      transfer : str or None
          If not `None`, will try to reuse pre-trained weights from yolo networks trained on other
          datasets.
      pretrained_base : boolean
          Whether fetch and load pretrained weights for base network.
      norm_layer : object
          Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
          Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
      norm_kwargs : dict
          Additional `norm_layer` arguments, for example `num_devices=4`
          for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
      Returns
      -------
      mxnet.gluon.HybridBlock
          Fully hybrid yolo3 network.
      """
      if pretrained:
          warnings.warn(
              "Custom models don't provide `pretrained` weights, ignored."
          )
      if transfer is None:
          base_net = get_mobilenet(
              multiplier=0.25,
              pretrained=pretrained_base,
              norm_layer=norm_layer,
              norm_kwargs=norm_kwargs,
              **kwargs,
          )
          stages = [
              base_net.features[:33],
              base_net.features[33:69],
              base_net.features[69:-2],
          ]
          anchors = [
              [10, 13, 16, 30, 33, 23],
              [30, 61, 62, 45, 59, 119],
              [116, 90, 156, 198, 373, 326],
          ]
          strides = [8, 16, 32]
          net = get_yolov3(
              "mobilenet0.25",
              stages,
              [256, 128, 128],
              anchors,
              strides,
              classes,
              "",
              norm_layer=norm_layer,
              norm_kwargs=norm_kwargs,
              **kwargs,
          )
      else:
          from ...model_zoo import get_model
  
          net = get_model(
              "yolo3_mobilenet0.25_" + str(transfer),
              pretrained=True,
              **kwargs,
          )
          reuse_classes = [x for x in classes if x in net.classes]
          net.reset_class(classes, reuse_weights=reuse_classes)
      return net
  
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  import warnings
  import numpy
  import mxnet
  from mxnet import gluon
  from mxnet import autograd
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from .darknet import _conv2d, darknet53
  from ..mobilenet import get_mobilenet
  from .yolo_target import YOLOV3TargetMerger
  from ...loss import YOLOV3Loss
label_desc: Use image node_id 11 for calling a global function with example usage: yolo3_mobilenet0_25_custom(classes, transfer, pretrained_base, pretrained, norm_layer, norm_kwargs) and returns: net