code: def get_fastscnn(
      dataset="citys",
      ctx=cpu(0),
      pretrained=False,
      root="~/.mxnet/models",
      **kwargs,
  ):
      r"""Fast-SCNN: Fast Semantic Segmentation Network
      Parameters
      ----------
      dataset : str, default cityscapes
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      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.
  
      Examples
      --------
      >>> model = get_fastscnn(dataset='citys')
      >>> print(model)
      """
      acronyms = {
          "citys": "citys",
      }
      from ..data import datasets
  
      model = FastSCNN(datasets[dataset].NUM_CLASS, ctx=ctx, **kwargs)
      model.classes = datasets[dataset].classes
  
      if pretrained:
          from .model_store import get_model_file
  
          model.load_parameters(
              get_model_file(
                  "fastscnn_%s" % (acronyms[dataset]),
                  tag=pretrained,
                  root=root,
              ),
              ctx=ctx,
          )
      return model
  
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 2 for calling a global function with example usage: get_fastscnn(dataset, ctx, pretrained, root) and returns: model