parameters: (pretrained=False, progress=True, **kwargs)
return_statement: return _resnet(
          "resnet152", Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs
      )
docstring: Constructs a ResNet-152 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
docstring_summary: Constructs a ResNet-152 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
docstring_tokens: Constructs a ResNet - 152 model . Args : pretrained ( bool ) : If True returns a model pre - trained on ImageNet progress ( bool ) : If True displays a progress bar of the download to stderr
function: def resnet152(pretrained=False, progress=True, **kwargs):
      """Constructs a ResNet-152 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _resnet(
          "resnet152", Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs
      )
function_tokens: def resnet152 ( pretrained = False , progress = True , * * kwargs ) : return _resnet ( "resnet152" , Bottleneck , [ 3 , 8 , 36 , 3 ] , pretrained , progress , * * kwargs )