code: def resnet101_v1d_73(
      pretrained=False, root="~/.mxnet/models", ctx=cpu(0), **kwargs
  ):
      """Constructs a ResNetV1d-101_2.2x model. Uses resnet101_v1d construction from resnetv1b.py
  
      Parameters
      ----------
      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.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      """
      model = ResNetV1b(
          BottleneckV1b,
          [3, 4, 23, 3],
          deep_stem=True,
          avg_down=True,
          name_prefix="resnetv1d_",
          **kwargs,
      )
      dirname = os.path.dirname(__file__)
      json_filename = os.path.join(
          dirname, "resnet%d_v%dd_%.1fx" % (101, 1, 2.2) + ".json"
      )
      with open(json_filename, "r") as jsonFile:
          params_shapes = json.load(jsonFile)
      if pretrained:
          from ..model_store import get_model_file
  
          params_file = get_model_file(
              "resnet%d_v%dd_%.1fx" % (101, 1, 2.2),
              tag=pretrained,
              root=root,
          )
          prune_gluon_block(
              model,
              model.name,
              params_shapes,
              params=ndarray.load(params_file),
              pretrained=True,
              ctx=ctx,
          )
      else:
          prune_gluon_block(
              model,
              model.name,
              params_shapes,
              params=None,
              pretrained=False,
              ctx=ctx,
          )
  
      if pretrained:
          from ...data import ImageNet1kAttr
  
          attrib = ImageNet1kAttr()
          model.synset = attrib.synset
          model.classes = attrib.classes
          model.classes_long = attrib.classes_long
      return model
  
file_path: gluon-cv/gluoncv/model_zoo/pruned_resnet/resnetv1b_pruned.py
imports: from __future__ import division
  import json
  import os
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet import ndarray
  from ..resnetv1b import ResNetV1b
  from ..resnetv1b import BasicBlockV1b
  from ..resnetv1b import BottleneckV1b
label_desc: Use image node_id 8 for calling a global function with example usage: resnet101_v1d_73(pretrained, root, ctx) and returns: model