egative_binomial.NegativeBinomial method) ](distributions.html#torch.distributions.negative_binomial.NegativeBinomial.log_prob)
    * [ (torch.distributions.normal.Normal method) ](distributions.html#torch.distributions.normal.Normal.log_prob)
    * [ (torch.distributions.one_hot_categorical.OneHotCategorical method) ](distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical.log_prob)
    * [ (torch.distributions.poisson.Poisson method) ](distributions.html#torch.distributions.poisson.Poisson.log_prob)
    * [ (torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli method) ](distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.log_prob)
    * [ (torch.distributions.studentT.StudentT method) ](distributions.html#torch.distributions.studentT.StudentT.log_prob)
    * [ (torch.distributions.transformed_distribution.TransformedDistribution method) ](distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.log_prob)
    * [ (torch.distributions.uniform.Uniform method) ](distributions.html#torch.distributions.uniform.Uniform.log_prob)
    * [ (torch.distributions.von_mises.VonMises method) ](distributions.html#torch.distributions.von_mises.VonMises.log_prob)
    * [ (torch.nn.AdaptiveLogSoftmaxWithLoss method) ](generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html#torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob)

|

  * [ log_softmax() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.log_softmax)
  * [ logaddexp() (in module torch) ](generated/torch.logaddexp.html#torch.logaddexp)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.logaddexp)
  * [ logaddexp2() (in module torch) ](generated/torch.logaddexp2.html#torch.logaddexp2)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.logaddexp2)
  * [ logcumsumexp() (in module torch) ](generated/torch.logcumsumexp.html#torch.logcumsumexp)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.logcumsumexp)
  * [ logdet() (in module torch) ](generated/torch.logdet.html#torch.logdet)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.logdet)
  * [ logical_and() (in module torch) ](generated/torch.logical_and.html#torch.logical_and)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.logical_and)
  * [ logical_and_() (torch.Tensor method) ](tensors.html#torch.Tensor.logical_and_)
  * [ logical_not() (in module torch) ](generated/torch.logical_not.html#torch.logical_not)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.logical_not)
  * [ logical_not_() (torch.Tensor method) ](tensors.html#torch.Tensor.logical_not_)
  * [ logical_or() (in module torch) ](generated/torch.logical_or.html#torch.logical_or)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.logical_or)
  * [ logical_or_() (torch.Tensor method) ](tensors.html#torch.Tensor.logical_or_)
  * [ logical_xor() (in module torch) ](generated/torch.logical_xor.html#torch.logical_xor)
    * [ (torch.Tensor method) ](tensors.html#torch.Tensor.logical_xor)
  * [ logical_xor_() (torch.Tensor method) ](tensors.html#torch.Tensor.logical_xor_)
  * [ LogitRelaxedBernoulli (class in torch.distributions.relaxed_bernoulli) ](distributions.html#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli)
  * [ logits (torch.distributions.bernoulli.Bernoulli attribute) ](distributions.html#torch.distributions.bernoulli.Bernoulli.logits)
    * [ (torch.distributions.binomial.Binomial attribute) ](distributions.html#torch.distributions.binomial.Binomial.logits)
    * [ (torch.distributions.categorical.Categorical attribute) ](distributions.html#torch.distributions.categorical.Categorical.logits)
    * [ (torch.distributions.continuous_bernoulli.ContinuousBernoulli attribute) ](distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits)
    * [ (torch.distributions.geometric.Geometric attribute) ](distributions.html#torch.distributions.geometric.Geometric.logits)
    * [ (torch.distri