parameters: (pretrained: bool = False, progress: bool = True, **kwargs: Any)
return_statement: return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
                     pretrained, progress, **kwargs)
docstring: r"""Wide ResNet-101-2 model from
      `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
  
      The model is the same as ResNet except for the bottleneck number of channels
      which is twice larger in every block. The number of channels in outer 1x1
      convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
      channels, and in Wide ResNet-50-2 has 2048-1024-2048.
  
      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"""Wide ResNet-101-2 model from
      `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
docstring_tokens: r Wide ResNet - 101 - 2 model from Wide Residual Networks <https : // arxiv . org / pdf / 1605 . 07146 . pdf > _ .
function: def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
      r"""Wide ResNet-101-2 model from
      `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
  
      The model is the same as ResNet except for the bottleneck number of channels
      which is twice larger in every block. The number of channels in outer 1x1
      convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
      channels, and in Wide ResNet-50-2 has 2048-1024-2048.
  
      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
      """
      kwargs['width_per_group'] = 64 * 2
      return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
                     pretrained, progress, **kwargs)
function_tokens: def wide_resnet101_2 ( pretrained : bool = False , progress : bool = True , * * kwargs : Any ) -> ResNet : kwargs [ 'width_per_group' ] = 64 * 2 return _resnet ( 'wide_resnet101_2' , Bottleneck , [ 3 , 4 , 23 , 3 ] , pretrained , progress , * * kwargs )