code: def get_cifar_resnet(
      version,
      num_layers,
      pretrained=False,
      ctx=cpu(),
      root=os.path.join("~", ".mxnet", "models"),
      **kwargs,
  ):
      r"""ResNet V1 model from `"Deep Residual Learning for Image Recognition"
      <http://arxiv.org/abs/1512.03385>`_ paper.
      ResNet V2 model from `"Identity Mappings in Deep Residual Networks"
      <https://arxiv.org/abs/1603.05027>`_ paper.
  
      Parameters
      ----------
      version : int
          Version of ResNet. Options are 1, 2.
      num_layers : int
          Numbers of layers. Needs to be an integer in the form of 6*n+2, e.g. 20, 56, 110, 164.
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/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`.
      """
      layers, channels = _get_resnet_spec(num_layers)
  
      resnet_class = resnet_net_versions[version - 1]
      block_class = resnet_block_versions[version - 1]
      net = resnet_class(block_class, layers, channels, **kwargs)
      if pretrained:
          from .model_store import get_model_file
  
          net.load_parameters(
              get_model_file(
                  "cifar_resnet%d_v%d" % (num_layers, version),
                  tag=pretrained,
                  root=root,
              ),
              ctx=ctx,
          )
      return net
  
imports: from __future__ import division
  import os
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet import cpu
label_desc: Use image node_id 3 for calling a global function with example usage: get_cifar_resnet(version, num_layers, pretrained, ctx, root) and returns: net