orch.distributions.independent.Independent method) ](distributions.html#torch.distributions.independent.Independent.sample)
    * [ (torch.distributions.mixture_same_family.MixtureSameFamily method) ](distributions.html#torch.distributions.mixture_same_family.MixtureSameFamily.sample)
    * [ (torch.distributions.multinomial.Multinomial method) ](distributions.html#torch.distributions.multinomial.Multinomial.sample)
    * [ (torch.distributions.negative_binomial.NegativeBinomial method) ](distributions.html#torch.distributions.negative_binomial.NegativeBinomial.sample)
    * [ (torch.distributions.normal.Normal method) ](distributions.html#torch.distributions.normal.Normal.sample)
    * [ (torch.distributions.one_hot_categorical.OneHotCategorical method) ](distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.sample)
    * [ (torch.distributions.poisson.Poisson method) ](distributions.html#torch.distributions.poisson.Poisson.sample)
    * [ (torch.distributions.transformed_distribution.TransformedDistribution method) ](distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.sample)
    * [ (torch.distributions.von_mises.VonMises method) ](distributions.html#torch.distributions.von_mises.VonMises.sample)
  * [ sample_n() (torch.distributions.distribution.Distribution method) ](distributions.html#torch.distributions.distribution.Distribution.sample_n)
  * [ Sampler (class in torch.utils.data) ](data.html#torch.utils.data.Sampler)
  * [ save() (in module torch) ](generated/torch.save.html#torch.save)
    * [ (in module torch.jit) ](generated/torch.jit.save.html#torch.jit.save)
    * [ (torch.jit.ScriptFunction method) ](generated/torch.jit.ScriptFunction.html#torch.jit.ScriptFunction.save)
    * [ (torch.jit.ScriptModule method) ](generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule.save)
  * [ save_for_backward() (torch.autograd.function._ContextMethodMixin method) ](autograd.html#torch.autograd.function._ContextMethodMixin.save_for_backward)
  * [ save_image() (in module torchvision.utils) ](torchvision/utils.html#torchvision.utils.save_image)
  * [ save_to_buffer() (torch.jit.ScriptFunction method) ](generated/torch.jit.ScriptFunction.html#torch.jit.ScriptFunction.save_to_buffer)
  * [ SBDataset (class in torchvision.datasets) ](torchvision/datasets.html#torchvision.datasets.SBDataset)
  * [ SBU (class in torchvision.datasets) ](torchvision/datasets.html#torchvision.datasets.SBU)
  * [ Scale (class in torchvision.transforms) ](torchvision/transforms.html#torchvision.transforms.Scale)
  * [ scale() (torch.cuda.amp.GradScaler method) ](amp.html#torch.cuda.amp.GradScaler.scale)
    * [ (torch.distributions.half_cauchy.HalfCauchy property) ](distributions.html#torch.distributions.half_cauchy.HalfCauchy.scale)
    * [ (torch.distributions.half_normal.HalfNormal property) ](distributions.html#torch.distributions.half_normal.HalfNormal.scale)
    * [ (torch.distributions.log_normal.LogNormal property) ](distributions.html#torch.distributions.log_normal.LogNormal.scale)
  * [ scale_tril (torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal attribute) ](distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.scale_tril)
    * [ (torch.distributions.multivariate_normal.MultivariateNormal attribute) ](distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.scale_tril)
  * [ scatter() (in module torch.cuda.comm) ](cuda.html#torch.cuda.comm.scatter)
    * [ (in module torch.distributed) ](distributed.html#torch.distributed.scatter)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.scatter)
  * [ scatter_() (torch.Tensor method) ](tensors.html#torch.Tensor.scatter_)
  * [ scatter_add() (torch.Tensor method) ](tensors.html#torch.Tensor.scatter_add)
  * [ scatter_add_() (torch.Tensor method) ](tensors.html#torch.Tensor.scatter_add_)
  * [ script() (in module torch.jit) ](generated/torch.jit.scr