code: def get_fcn_resnet50_voc(**kwargs):
      r"""FCN model with base network ResNet-50 pre-trained on Pascal VOC dataset
      from the paper `"Fully Convolutional Network for semantic segmentation"
      <https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf>`_
  
      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.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
  
      Examples
      --------
      >>> model = get_fcn_resnet50_voc(pretrained=True)
      >>> print(model)
      """
      return get_fcn("pascal_voc", "resnet50", **kwargs)
  
file_path: gluon-cv/gluoncv/model_zoo/fcn.py
imports: from __future__ import division
  from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from .segbase import SegBaseModel
label_desc: Use image node_id 2 for calling a global function with example usage: get_fcn_resnet50_voc() and returns: get_fcn