code: def get_Siam_RPN(
      base_name,
      bz=1,
      is_train=False,
      pretrained=False,
      ctx=mx.cpu(0),
      root="~/.mxnet/models",
      **kwargs,
  ):
      """get Siam_RPN net and get pretrained model if have pretrained
  
      Parameters
      ----------
      base_name : str
          Backbone model name
      bz : int
          batch size for train, bz = 1 if test
      is_train : str
          is_train is True if train, False if test
      pretrained : bool or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      ctx : mxnet.Context
          Context such as mx.cpu(), mx.gpu(0).
      root : str
          Model weights storing path.
  
      Returns
      -------
      HybridBlock
          A SiamRPN Tracking network.
      """
      net = SiamRPN(bz=bz, is_train=is_train, ctx=ctx)
      if pretrained:
          from gluoncv.model_zoo.model_store import get_model_file
  
          net.load_parameters(
              get_model_file(
                  "siamrpn_%s" % (base_name), tag=pretrained, root=root
              ),
              ctx=ctx,
          )
  
      return net
  
file_path: gluon-cv/gluoncv/model_zoo/siamrpn/siam_net.py
imports: from mxnet.gluon.block import HybridBlock
  from mxnet.context import cpu
  import mxnet
  from gluoncv.model_zoo.siamrpn.siam_alexnet import alexnetlegacy
  from gluoncv.model_zoo.siamrpn.siam_rpn import DepthwiseRPN
label_desc: Use image node_id 1 for calling a global function with example usage: get_Siam_RPN(base_name, bz, is_train, pretrained, ctx, root) and returns: net