code: def get_segmentation_model(model, **kwargs):
      from .fcn import get_fcn
      from .pspnet import get_psp
      from .deeplabv3 import get_deeplab
      from .deeplabv3_plus import get_deeplab_plus
      from .deeplabv3b_plus import get_deeplabv3b_plus
      from .icnet import get_icnet
      from .fastscnn import get_fastscnn
      from .danet import get_danet
  
      models = {
          "fcn": get_fcn,
          "psp": get_psp,
          "deeplab": get_deeplab,
          "deeplabplus": get_deeplab_plus,
          "deeplabplusv3b": get_deeplabv3b_plus,
          "icnet": get_icnet,
          "fastscnn": get_fastscnn,
          "danet": get_danet,
      }
      return models[model](**kwargs)
  
imports: import math
  import warnings
  import numpy
  import mxnet
  from mxnet.ndarray import NDArray
  from mxnet.gluon.nn import HybridBlock
  from ..utils.parallel import parallel_apply
  from .resnetv1b import resnet18_v1b, resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
  from .resnest import resnest50, resnest101, resnest200, resnest269
  from ..utils.parallel import tuple_map
label_desc: Use image node_id 1 for calling a global function with example usage: get_segmentation_model(model) and returns: models