parameters: (pretrained: bool = False, progress: bool = True, **kwargs: Any)
return_statement: return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                     **kwargs)
docstring: r"""ResNet-34 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
  
      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: r"""ResNet-34 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
docstring_tokens: r ResNet - 34 model from Deep Residual Learning for Image Recognition <https : // arxiv . org / pdf / 1512 . 03385 . pdf > _ .
function: def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""ResNet-34 model from
      `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
  
      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('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                     **kwargs)
function_tokens: def resnet34 ( pretrained : bool = False , progress : bool = True , * * kwargs : Any ) -> ResNet : return _resnet ( 'resnet34' , BasicBlock , [ 3 , 4 , 6 , 3 ] , pretrained , progress , * * kwargs )