` entropy ` (  )  [ [source]
](_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.entropy)
¶

    

` enumerate_support ` (  _expand=True_ )  [ [source]
](_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.enumerate_support)
¶

    

` expand ` (  _batch_shape_ , __instance=None_ )  [ [source]
](_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.expand)
¶

    

` has_enumerate_support ` _= True_ ¶

    

` log_prob ` (  _value_ )  [ [source]
](_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.log_prob)
¶

    

_property_ ` logits ` ¶

    

_property_ ` mean ` ¶

    

_property_ ` param_shape ` ¶

    

_property_ ` probs ` ¶

    

` sample ` (  _sample_shape=torch.Size([])_ )  [ [source]
](_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.sample)
¶

    

` support ` _= Simplex()_ ¶

    

_property_ ` variance ` ¶

    

##  Pareto  ¶

_class_ ` torch.distributions.pareto. ` ` Pareto ` (  _scale_ , _alpha_ ,
_validate_args=None_ )  [ [source]
](_modules/torch/distributions/pareto.html#Pareto) ¶

    

Bases:  ` torch.distributions.transformed_distribution.TransformedDistribution
`

Samples from a Pareto Type 1 distribution.

Example:

    
    
    >>> m = Pareto(torch.tensor([1.0]), torch.tensor([1.0]))
    >>> m.sample()  # sample from a Pareto distribution with scale=1 and alpha=1
    tensor([ 1.5623])
    

Parameters

    

  * **scale** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") _or_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – Scale parameter of the distribution 

  * **alpha** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") _or_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – Shape parameter of the distribution 

` arg_constraints ` _= {'alpha': GreaterThan(lower_bound=0.0), 'scale':
GreaterThan(lower_bound=0.0)}_ ¶

    

` entropy ` (  )  [ [source]
](_modules/torch/distributions/pareto.html#Pareto.entropy) ¶

    

` expand ` (  _batch_shape_ , __instance=None_ )  [ [source]
](_modules/torch/distributions/pareto.html#Pareto.expand) ¶

    

_property_ ` mean ` ¶

    

_property_ ` support ` ¶

    

_property_ ` variance ` ¶

    

##  Poisson  ¶

_class_ ` torch.distributions.poisson. ` ` Poisson ` (  _rate_ ,
_validate_args=None_ )  [ [source]
](_modules/torch/distributions/poisson.html#Poisson) ¶

    

Bases:  ` torch.distributions.exp_family.ExponentialFamily  `

Creates a Poisson distribution parameterized by ` rate  ` , the rate
parameter.

Samples are nonnegative integers, with a pmf given by

r  a  t  e  k  e  −  r  a  t  e  k  !  \mathrm{rate}^k
\frac{e^{-\mathrm{rate}}}{k!}  r  a  t  e  k  k  !  e  −  r  a  t  e  ​

Example:

    
    
    >>> m = Poisson(torch.tensor([4]))
    >>> m.sample()
    tensor([ 3.])
    

Parameters

    

**rate** ( _Number_ _,_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor")
) – the rate parameter

` arg_constraints ` _= {'rate': GreaterThan(lower_bound=0.0)}_ ¶

    

` expand ` (  _batch_shape_ , __instance=None_ )  [ [source]
](_modules/torch/distributions/poisson.html#Poisson.expand) ¶

    

` log_prob ` (  _value_ )  [ [source]
](_modules/torch/distributions/poisson.html#Poisson.log_prob) ¶

    

_property_ ` mean ` ¶

    

` sample ` (  _sample_shape=torch.Size([])_ )  [ [source]
](_modules/torch/distributions/poisson.html#Poisson.sample) ¶

    

` support ` _= IntegerGreaterThan(lower_bound=0)_ ¶

    

_property_ ` variance ` ¶

    

##  RelaxedBernoulli  ¶

_class_ ` torch.distributions.relaxed_bernoulli. ` ` RelaxedBernoulli ` (
_temperature_ , _probs=None_ , _logits=None_ , _validate_args=None_ )  [
[source]
](_modules/torch/distributions/relaxed_bernoulli.html#RelaxedBernoulli) ¶

    

Bases:  ` torch.distributions.transformed_distribution.TransformedDistribution
`

Creates a RelaxedBernoulli distribution, parametrized by