code: def get_hrnet(
      model_name,
      stage_interp_type="nearest",
      purpose="cls",
      pretrained=False,
      ctx=cpu(),
      root="~/.mxnet/models",
      norm_layer=BatchNorm,
      norm_kwargs=None,
      num_classes=1000,
      **kwargs,
  ):
      r"""HRNet model from the
      `"Deep High-Resolution Representation Learning for Visual Recognition"
      <https://arxiv.org/pdf/1908.07919>`_ paper.
  
      Parameters
      ----------
      model_name : string
          The name of hrnet models: w18_small_v1/w18_small_v2/w30/w32/w40/w42/w48.
      stage_interp_type : string
          The interpolation type for upsample in each stage, nearest, bilinear and
          bilinear_like are supported.
      purpose: string
          The purpose of model, cls and seg are supported.
      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 : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default $MXNET_HOME/models
          Location for keeping the model parameters.
      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`.
      """
      if model_name not in HRNET_SPEC.keys():
          raise NotImplementedError
  
      spec = HRNET_SPEC[model_name]
  
      if purpose == "cls":
          net = HighResolutionClsNet(
              spec,
              stage_interp_type,
              norm_layer,
              norm_kwargs,
              num_classes,
              **kwargs,
          )
          from ..data import ImageNet1kAttr
  
          attrib = ImageNet1kAttr()
          net.synset = attrib.synset
          net.classes = attrib.classes
          net.classes_long = attrib.classes_long
      elif purpose == "seg":
          net = HighResolutionSegNet(
              spec,
              stage_interp_type,
              norm_layer,
              norm_kwargs,
              num_classes,
              **kwargs,
          )
      else:
          raise NotImplementedError
  
      if pretrained:
          from .model_store import get_model_file
  
          net.load_parameters(
              get_model_file(
                  "hrnet_%s_%s" % (model_name, purpose),
                  tag=pretrained,
                  root=root,
              ),
              ctx=ctx,
          )
      return net
  
imports: import numpy
  from mxnet.gluon import contrib
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.context import cpu
  from .resnet import BasicBlockV1, BottleneckV1, _conv3x3
label_desc: Use image node_id 1 for calling a global function with example usage: get_hrnet(model_name, stage_interp_type, purpose, pretrained, ctx, root, norm_layer, norm_kwargs, num_classes) and returns: net