parameters: (pretrained=False, root='~/.encoding/models', **kwargs)
return_statement: return get_fcn('pcontext', 'resnet50', pretrained, root=root, aux=False, **kwargs)
docstring: r"""EncNet-PSP model from the paper `"Context Encoding for Semantic Segmentation"
      <https://arxiv.org/pdf/1803.08904.pdf>`_
  
      Parameters
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.encoding/models'
          Location for keeping the model parameters.
  
  
      Examples
      --------
      >>> model = get_fcn_resnet50_pcontext(pretrained=True)
      >>> print(model)
docstring_summary: r"""EncNet-PSP model from the paper `"Context Encoding for Semantic Segmentation"
      <https://arxiv.org/pdf/1803.08904.pdf>`_
docstring_tokens: r EncNet - PSP model from the paper Context Encoding for Semantic Segmentation <https : // arxiv . org / pdf / 1803 . 08904 . pdf > _
function: def get_fcn_resnet50_pcontext(pretrained=False, root='~/.encoding/models', **kwargs):
      r"""EncNet-PSP model from the paper `"Context Encoding for Semantic Segmentation"
      <https://arxiv.org/pdf/1803.08904.pdf>`_
  
      Parameters
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.encoding/models'
          Location for keeping the model parameters.
  
  
      Examples
      --------
      >>> model = get_fcn_resnet50_pcontext(pretrained=True)
      >>> print(model)
      """
      return get_fcn('pcontext', 'resnet50', pretrained, root=root, aux=False, **kwargs)
function_tokens: def get_fcn_resnet50_pcontext ( pretrained = False , root = '~/.encoding/models' , * * kwargs ) : return get_fcn ( 'pcontext' , 'resnet50' , pretrained , root = root , aux = False , * * kwargs )