)
  * [ uniform_() (in module torch.nn.init) ](nn.init.html#torch.nn.init.uniform_)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.uniform_)
  * [ unique() (in module torch) ](generated/torch.unique.html#torch.unique)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.unique)
  * [ unique_consecutive() (in module torch) ](generated/torch.unique_consecutive.html#torch.unique_consecutive)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.unique_consecutive)
  * [ unscale_() (torch.cuda.amp.GradScaler method) ](amp.html#torch.cuda.amp.GradScaler.unscale_)
  * [ unsorted_indices() (torch.nn.utils.rnn.PackedSequence property) ](generated/torch.nn.utils.rnn.PackedSequence.html#torch.nn.utils.rnn.PackedSequence.unsorted_indices)

|

  * [ unsqueeze() (in module torch) ](generated/torch.unsqueeze.html#torch.unsqueeze)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.unsqueeze)
  * [ unsqueeze_() (torch.Tensor method) ](tensors.html#torch.Tensor.unsqueeze_)
  * [ unused() (in module torch.jit) ](generated/torch.jit.unused.html#torch.jit.unused)
  * [ update() (torch.cuda.amp.GradScaler method) ](amp.html#torch.cuda.amp.GradScaler.update)
    * [ (torch.nn.ModuleDict method) ](generated/torch.nn.ModuleDict.html#torch.nn.ModuleDict.update)
    * [ (torch.nn.ParameterDict method) ](generated/torch.nn.ParameterDict.html#torch.nn.ParameterDict.update)
  * [ Upsample (class in torch.nn) ](generated/torch.nn.Upsample.html#torch.nn.Upsample)
  * [ upsample() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.upsample)
    * [ (in module torch.nn.quantized.functional) ](quantization.html#torch.nn.quantized.functional.upsample)
  * [ upsample_bilinear() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.upsample_bilinear)
    * [ (in module torch.nn.quantized.functional) ](quantization.html#torch.nn.quantized.functional.upsample_bilinear)
  * [ upsample_nearest() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.upsample_nearest)
    * [ (in module torch.nn.quantized.functional) ](quantization.html#torch.nn.quantized.functional.upsample_nearest)
  * [ UpsamplingBilinear2d (class in torch.nn) ](generated/torch.nn.UpsamplingBilinear2d.html#torch.nn.UpsamplingBilinear2d)
  * [ UpsamplingNearest2d (class in torch.nn) ](generated/torch.nn.UpsamplingNearest2d.html#torch.nn.UpsamplingNearest2d)
  * [ USPS (class in torchvision.datasets) ](torchvision/datasets.html#torchvision.datasets.USPS)

  
---|---  
  
##  V

  * [ values() (torch.nn.ModuleDict method) ](generated/torch.nn.ModuleDict.html#torch.nn.ModuleDict.values)
    * [ (torch.nn.ParameterDict method) ](generated/torch.nn.ParameterDict.html#torch.nn.ParameterDict.values)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.values)
  * [ vander() (in module torch) ](generated/torch.vander.html#torch.vander)
  * [ var() (in module torch) ](generated/torch.var.html#torch.var) , [ [1] ](generated/torch.var.html#torch.var)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.var)
  * [ var_mean() (in module torch) ](generated/torch.var_mean.html#torch.var_mean) , [ [1] ](generated/torch.var_mean.html#torch.var_mean)
  * [ variance (torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal attribute) ](distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.variance)
    * [ (torch.distributions.von_mises.VonMises attribute) ](distributions.html#torch.distributions.von_mises.VonMises.variance)
  * [ variance() (torch.distributions.bernoulli.Bernoulli property) ](distributions.html#torch.distributions.bernoulli.Bernoulli.variance)
    * [ (torch.distributions.beta.Beta property) ](distributions.html#torch.distributions.beta.Beta.variance)
    * [ (torch.distributions.binomial.Binomial property) ](distributions.html#torch.distributions.binomial.Binomial.variance)
    * [ (torch.distributions.categorical.Categorical property) ](distribution