code: def get_net(
      classes,
      model_name="",
      use_pretrained=False,
      param_path="",
      ctx=None,
      **kwargs,
  ):
      assert (
          model_name in ssd_base_models
      ), "the model name is not supported, where the supported models are {}".format(
          ssd_base_models.keys()
      )
      if use_pretrained:
          # use off-the-shelf GluonCV pretrained SSD models
          net = ssd_base_models[model_name](
              pretrained=use_pretrained,
              pretrained_base=False,
              ctx=ctx,
              **kwargs,
          )
      else:
          # use finetuned model weights or customized trained model weights
          net = ssd_base_models[model_name](
              pretrained_base=False, ctx=ctx, **kwargs
          )
          assert (
              param_path != ""
          ), "Please provide the pretrained model weights if you are not using GluonCV pretrained detectors."
          net.load_parameters(param_path, ctx=ctx)
      net.hybridize()
      return net
  
file_path: gluon-cv/gluoncv/model_zoo/smot/general_detector.py
imports: import logging
  import numpy
  import mxnet
  from gluoncv.data import COCODetection
  from .utils import mxnet_frame_preprocessing, timeit_context
  from .utils import remap_bboxes
  from .presets import *
label_desc: Use image node_id 1 for calling a global function with example usage: get_net(classes, model_name, use_pretrained, param_path, ctx) and returns: net