ndOptions.rpc_timeout)
  * [ RpcBackendOptions (class in torch.distributed.rpc) ](rpc.html#torch.distributed.rpc.RpcBackendOptions)
  * [ Rprop (class in torch.optim) ](optim.html#torch.optim.Rprop)
  * [ RRef (class in torch.distributed.rpc) ](rpc.html#torch.distributed.rpc.RRef)
  * [ RReLU (class in torch.nn) ](generated/torch.nn.RReLU.html#torch.nn.RReLU)
  * [ rrelu() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.rrelu)
  * [ rrelu_() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.rrelu_)
  * [ rsample() (torch.distributions.beta.Beta method) ](distributions.html#torch.distributions.beta.Beta.rsample)
    * [ (torch.distributions.cauchy.Cauchy method) ](distributions.html#torch.distributions.cauchy.Cauchy.rsample)
    * [ (torch.distributions.continuous_bernoulli.ContinuousBernoulli method) ](distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample)
    * [ (torch.distributions.dirichlet.Dirichlet method) ](distributions.html#torch.distributions.dirichlet.Dirichlet.rsample)
    * [ (torch.distributions.distribution.Distribution method) ](distributions.html#torch.distributions.distribution.Distribution.rsample)
    * [ (torch.distributions.exponential.Exponential method) ](distributions.html#torch.distributions.exponential.Exponential.rsample)
    * [ (torch.distributions.fishersnedecor.FisherSnedecor method) ](distributions.html#torch.distributions.fishersnedecor.FisherSnedecor.rsample)
    * [ (torch.distributions.gamma.Gamma method) ](distributions.html#torch.distributions.gamma.Gamma.rsample)
    * [ (torch.distributions.independent.Independent method) ](distributions.html#torch.distributions.independent.Independent.rsample)
    * [ (torch.distributions.laplace.Laplace method) ](distributions.html#torch.distributions.laplace.Laplace.rsample)
    * [ (torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal method) ](distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.rsample)
    * [ (torch.distributions.multivariate_normal.MultivariateNormal method) ](distributions.html#torch.distributions.multivariate_normal.MultivariateNormal.rsample)
    * [ (torch.distributions.normal.Normal method) ](distributions.html#torch.distributions.normal.Normal.rsample)
    * [ (torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli method) ](distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.rsample)
    * [ (torch.distributions.studentT.StudentT method) ](distributions.html#torch.distributions.studentT.StudentT.rsample)
    * [ (torch.distributions.transformed_distribution.TransformedDistribution method) ](distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.rsample)
    * [ (torch.distributions.uniform.Uniform method) ](distributions.html#torch.distributions.uniform.Uniform.rsample)
  * [ rsqrt() (in module torch) ](generated/torch.rsqrt.html#torch.rsqrt)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.rsqrt)
  * [ rsqrt_() (torch.Tensor method) ](tensors.html#torch.Tensor.rsqrt_)

  
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##  S

  * [ sample() (torch.distributions.bernoulli.Bernoulli method) ](distributions.html#torch.distributions.bernoulli.Bernoulli.sample)
    * [ (torch.distributions.binomial.Binomial method) ](distributions.html#torch.distributions.binomial.Binomial.sample)
    * [ (torch.distributions.categorical.Categorical method) ](distributions.html#torch.distributions.categorical.Categorical.sample)
    * [ (torch.distributions.continuous_bernoulli.ContinuousBernoulli method) ](distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample)
    * [ (torch.distributions.distribution.Distribution method) ](distributions.html#torch.distributions.distribution.Distribution.sample)
    * [ (torch.distributions.geometric.Geometric method) ](distributions.html#torch.distributions.geometric.Geometric.sample)
    * [ (t