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

    

` logits ` [ [source]
](_modules/torch/distributions/geometric.html#Geometric.logits) ¶

    

_property_ ` mean ` ¶

    

` probs ` [ [source]
](_modules/torch/distributions/geometric.html#Geometric.probs) ¶

    

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

    

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

    

_property_ ` variance ` ¶

    

##  Gumbel  ¶

_class_ ` torch.distributions.gumbel. ` ` Gumbel ` (  _loc_ , _scale_ ,
_validate_args=None_ )  [ [source]
](_modules/torch/distributions/gumbel.html#Gumbel) ¶

    

Bases:  ` torch.distributions.transformed_distribution.TransformedDistribution
`

Samples from a Gumbel Distribution.

Examples:

    
    
    >>> m = Gumbel(torch.tensor([1.0]), torch.tensor([2.0]))
    >>> m.sample()  # sample from Gumbel distribution with loc=1, scale=2
    tensor([ 1.0124])
    

Parameters

    

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

  * **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 

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

    

` entropy ` (  )  [ [source]
](_modules/torch/distributions/gumbel.html#Gumbel.entropy) ¶

    

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

    

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

    

_property_ ` mean ` ¶

    

_property_ ` stddev ` ¶

    

` support ` _= Real()_ ¶

    

_property_ ` variance ` ¶

    

##  HalfCauchy  ¶

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

    

Bases:  ` torch.distributions.transformed_distribution.TransformedDistribution
`

Creates a half-normal distribution parameterized by  scale  where:

    
    
    X ~ Cauchy(0, scale)
    Y = |X| ~ HalfCauchy(scale)
    

Example:

    
    
    >>> m = HalfCauchy(torch.tensor([1.0]))
    >>> m.sample()  # half-cauchy distributed with scale=1
    tensor([ 2.3214])
    

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 of the full Cauchy distribution

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

    

` cdf ` (  _value_ )  [ [source]
](_modules/torch/distributions/half_cauchy.html#HalfCauchy.cdf) ¶

    

` entropy ` (  )  [ [source]
](_modules/torch/distributions/half_cauchy.html#HalfCauchy.entropy) ¶

    

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

    

` has_rsample ` _= True_ ¶

    

` icdf ` (  _prob_ )  [ [source]
](_modules/torch/distributions/half_cauchy.html#HalfCauchy.icdf) ¶

    

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

    

_property_ ` mean ` ¶

    

_property_ ` scale ` ¶

    

` support ` _= GreaterThan(lower_bound=0.0)_ ¶

    

_property_ ` variance ` ¶

    

##  HalfNormal  ¶

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

    

Bases:  ` torch.distributions.transformed_distribution.TransformedDistribution
`

Creates a half-normal distribution parameterized by  scale  where:

    
    
    X ~ Normal(0, scal