eibull
` ,  ` RelaxedBernoulli  ` and  ` RelaxedOneHotCategorical  `

` arg_constraints ` _= {}_ ¶

    

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

    

Computes the cumulative distribution function by inverting the transform(s)
and computing the score of the base distribution.

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

    

_property_ ` has_rsample ` ¶

    

` icdf ` (  _value_ )  [ [source]
](_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.icdf)
¶

    

Computes the inverse cumulative distribution function using transform(s) and
computing the score of the base distribution.

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

    

Scores the sample by inverting the transform(s) and computing the score using
the score of the base distribution and the log abs det jacobian.

` rsample ` (  _sample_shape=torch.Size([])_ )  [ [source]
](_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.rsample)
¶

    

Generates a sample_shape shaped reparameterized sample or sample_shape shaped
batch of reparameterized samples if the distribution parameters are batched.
Samples first from base distribution and applies  transform()  for every
transform in the list.

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

    

Generates a sample_shape shaped sample or sample_shape shaped batch of samples
if the distribution parameters are batched. Samples first from base
distribution and applies  transform()  for every transform in the list.

_property_ ` support ` ¶

    

##  Uniform  ¶

_class_ ` torch.distributions.uniform. ` ` Uniform ` (  _low_ , _high_ ,
_validate_args=None_ )  [ [source]
](_modules/torch/distributions/uniform.html#Uniform) ¶

    

Bases:  ` torch.distributions.distribution.Distribution  `

Generates uniformly distributed random samples from the half-open interval `
[low,  high)  ` .

Example:

    
    
    >>> m = Uniform(torch.tensor([0.0]), torch.tensor([5.0]))
    >>> m.sample()  # uniformly distributed in the range [0.0, 5.0)
    tensor([ 2.3418])
    

Parameters

    

  * **low** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") _or_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – lower range (inclusive). 

  * **high** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") _or_ [ _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – upper range (exclusive). 

` arg_constraints ` _= {'high': Dependent(), 'low': Dependent()}_ ¶

    

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

    

` entropy ` (  )  [ [source]
](_modules/torch/distributions/uniform.html#Uniform.entropy) ¶

    

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

    

` has_rsample ` _= True_ ¶

    

` icdf ` (  _value_ )  [ [source]
](_modules/torch/distributions/uniform.html#Uniform.icdf) ¶

    

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

    

_property_ ` mean ` ¶

    

` rsample ` (  _sample_shape=torch.Size([])_ )  [ [source]
](_modules/torch/distributions/uniform.html#Uniform.rsample) ¶

    

_property_ ` stddev ` ¶

    

_property_ ` support ` ¶

    

_property_ ` variance ` ¶

    

##  VonMises  ¶

_class_ ` torch.distributions.von_mises. ` ` VonMises ` (  _loc_ ,
_concentration_ , _validate_args=None_ )  [ [source]
](_modules/torch/distributions/von_mises.html#VonMises) ¶

    

Bases:  ` torch.distributions.distri