g.set_sharing_strategy)
  * [ set_state() (torch.Generator method) ](generated/torch.Generator.html#torch.Generator.set_state)
  * [ set_video_backend() (in module torchvision) ](torchvision/index.html#torchvision.set_video_backend)
  * [ SGD (class in torch.optim) ](optim.html#torch.optim.SGD)
  * [ shape_as_tensor() (in module torch.onnx.operators) ](onnx.html#torch.onnx.operators.shape_as_tensor)
  * [ share_memory_() (torch.FloatStorage method) ](storage.html#torch.FloatStorage.share_memory_)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.share_memory_)
  * [ short() (torch.FloatStorage method) ](storage.html#torch.FloatStorage.short)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.short)
  * [ show() (in module torch.__config__) ](__config__.html#torch.__config__.show)
  * [ shufflenet_v2_x0_5() (in module torchvision.models) ](torchvision/models.html#torchvision.models.shufflenet_v2_x0_5)
  * [ shufflenet_v2_x1_0() (in module torchvision.models) ](torchvision/models.html#torchvision.models.shufflenet_v2_x1_0)
  * [ shufflenet_v2_x1_5() (in module torchvision.models) ](torchvision/models.html#torchvision.models.shufflenet_v2_x1_5)
  * [ shufflenet_v2_x2_0() (in module torchvision.models) ](torchvision/models.html#torchvision.models.shufflenet_v2_x2_0)
  * [ shutdown() (in module torch.distributed.rpc) ](rpc.html#torch.distributed.rpc.shutdown)
  * [ Sigmoid (class in torch.nn) ](generated/torch.nn.Sigmoid.html#torch.nn.Sigmoid)
  * [ sigmoid() (in module torch) ](generated/torch.sigmoid.html#torch.sigmoid)
    * [ (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.sigmoid)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.sigmoid)
  * [ sigmoid_() (torch.Tensor method) ](tensors.html#torch.Tensor.sigmoid_)
  * [ SigmoidTransform (class in torch.distributions.transforms) ](distributions.html#torch.distributions.transforms.SigmoidTransform)
  * [ sign() (in module torch) ](generated/torch.sign.html#torch.sign)
    * [ (torch.distributions.transforms.Transform property) ](distributions.html#torch.distributions.transforms.Transform.sign)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.sign)
  * [ sign_() (torch.Tensor method) ](tensors.html#torch.Tensor.sign_)
  * [ sin() (in module torch) ](generated/torch.sin.html#torch.sin)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.sin)
  * [ sin_() (torch.Tensor method) ](tensors.html#torch.Tensor.sin_)
  * [ sinh() (in module torch) ](generated/torch.sinh.html#torch.sinh)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.sinh)
  * [ sinh_() (torch.Tensor method) ](tensors.html#torch.Tensor.sinh_)
  * [ size() (torch.FloatStorage method) ](storage.html#torch.FloatStorage.size)
    * [ (torch.sparse.FloatTensor method) ](sparse.html#torch.sparse.FloatTensor.size)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.size)
  * [ slogdet() (in module torch) ](generated/torch.slogdet.html#torch.slogdet)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.slogdet)
  * [ smooth_l1_loss() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.smooth_l1_loss)
  * [ SmoothL1Loss (class in torch.nn) ](generated/torch.nn.SmoothL1Loss.html#torch.nn.SmoothL1Loss)
  * [ SobolEngine (class in torch.quasirandom) ](generated/torch.quasirandom.SobolEngine.html#torch.quasirandom.SobolEngine)
  * [ soft_margin_loss() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.soft_margin_loss)
  * [ SoftMarginLoss (class in torch.nn) ](generated/torch.nn.SoftMarginLoss.html#torch.nn.SoftMarginLoss)
  * [ Softmax (class in torch.nn) ](generated/torch.nn.Softmax.html#torch.nn.Softmax)
  * [ softmax() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.softmax)
  * [ Softmax2d (class in torch.nn) ](generated/torch.nn.Softmax2d.html#torch.nn.Softmax2d)
  * [ SoftmaxTransform (class in torch.distributions.transforms) ](distributions.html#torch.distributions.transforms.