html#torch.quantization.ObserverBase)
  * [ one_hot() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.one_hot)
  * [ OneCycleLR (class in torch.optim.lr_scheduler) ](optim.html#torch.optim.lr_scheduler.OneCycleLR)
  * [ OneHotCategorical (class in torch.distributions.one_hot_categorical) ](distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical)
  * [ ones() (in module torch) ](generated/torch.ones.html#torch.ones)
  * [ ones_() (in module torch.nn.init) ](nn.init.html#torch.nn.init.ones_)
  * [ ones_like() (in module torch) ](generated/torch.ones_like.html#torch.ones_like)
  * [ optimize_for_mobile() (in module torch.utils.mobile_optimizer) ](mobile_optimizer.html#torch.utils.mobile_optimizer.optimize_for_mobile)

|

  * [ Optimizer (class in torch.optim) ](optim.html#torch.optim.Optimizer)
  * [ orgqr() (in module torch) ](generated/torch.orgqr.html#torch.orgqr)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.orgqr)
  * [ ormqr() (in module torch) ](generated/torch.ormqr.html#torch.ormqr)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.ormqr)
  * [ orthogonal_() (in module torch.nn.init) ](nn.init.html#torch.nn.init.orthogonal_)
  * [ owner() (torch.distributed.rpc.RRef method) ](rpc.html#torch.distributed.rpc.RRef.owner)
  * [ owner_name() (torch.distributed.rpc.RRef method) ](rpc.html#torch.distributed.rpc.RRef.owner_name)

  
---|---  
  
##  P

  * [ pack_padded_sequence() (in module torch.nn.utils.rnn) ](generated/torch.nn.utils.rnn.pack_padded_sequence.html#torch.nn.utils.rnn.pack_padded_sequence)
  * [ pack_sequence() (in module torch.nn.utils.rnn) ](generated/torch.nn.utils.rnn.pack_sequence.html#torch.nn.utils.rnn.pack_sequence)
  * [ PackedSequence (class in torch.nn.utils.rnn) ](generated/torch.nn.utils.rnn.PackedSequence.html#torch.nn.utils.rnn.PackedSequence)
  * [ Pad (class in torchvision.transforms) ](torchvision/transforms.html#torchvision.transforms.Pad)
  * [ pad() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.pad)
    * [ (in module torchvision.transforms.functional) ](torchvision/transforms.html#torchvision.transforms.functional.pad)
  * [ pad_packed_sequence() (in module torch.nn.utils.rnn) ](generated/torch.nn.utils.rnn.pad_packed_sequence.html#torch.nn.utils.rnn.pad_packed_sequence)
  * [ pad_sequence() (in module torch.nn.utils.rnn) ](generated/torch.nn.utils.rnn.pad_sequence.html#torch.nn.utils.rnn.pad_sequence)
  * [ pairwise_distance() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.pairwise_distance)
  * [ PairwiseDistance (class in torch.nn) ](generated/torch.nn.PairwiseDistance.html#torch.nn.PairwiseDistance)
  * [ parallel_info() (in module torch.__config__) ](__config__.html#torch.__config__.parallel_info)
  * [ param_shape() (torch.distributions.bernoulli.Bernoulli property) ](distributions.html#torch.distributions.bernoulli.Bernoulli.param_shape)
    * [ (torch.distributions.binomial.Binomial property) ](distributions.html#torch.distributions.binomial.Binomial.param_shape)
    * [ (torch.distributions.categorical.Categorical property) ](distributions.html#torch.distributions.categorical.Categorical.param_shape)
    * [ (torch.distributions.continuous_bernoulli.ContinuousBernoulli property) ](distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.param_shape)
    * [ (torch.distributions.multinomial.Multinomial property) ](distributions.html#torch.distributions.multinomial.Multinomial.param_shape)
    * [ (torch.distributions.negative_binomial.NegativeBinomial property) ](distributions.html#torch.distributions.negative_binomial.NegativeBinomial.param_shape)
    * [ (torch.distributions.one_hot_categorical.OneHotCategorical property) ](distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.param_shape)
    * [ (torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli property) ](distributions.html#torch.distributions.relaxed_be