code: def resnet101_v1d(
      pretrained=False, root="~/.mxnet/models", ctx=cpu(0), **kwargs
  ):
      """Constructs a ResNetV1d-101 model.
  
      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.
      dilated: bool, default False
          Whether to apply dilation strategy to ResNetV1b, yielding a stride 8 model.
      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`.
      """
      model = ResNetV1b(
          BottleneckV1b,
          [3, 4, 23, 3],
          deep_stem=True,
          avg_down=True,
          name_prefix="resnetv1d_",
          **kwargs,
      )
      if pretrained:
          from .model_store import get_model_file
  
          model.load_parameters(
              get_model_file(
                  "resnet%d_v%dd" % (101, 1), tag=pretrained, root=root
              ),
              ctx=ctx,
          )
          from ..data import ImageNet1kAttr
  
          attrib = ImageNet1kAttr()
          model.synset = attrib.synset
          model.classes = attrib.classes
          model.classes_long = attrib.classes_long
      return model
  
imports: from __future__ import division
  from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
label_desc: Use image node_id 12 for calling a global function with example usage: resnet101_v1d(pretrained, root, ctx) and returns: model