
    ȅiU                     D    S SK Jr  S SKJr  S SKJr  S/r " S S\5      rg)    )Tensor)constraints)GammaChi2c                      ^  \ rS rSrSrS\R                  0r SS\\	-  S\
S-  SS4U 4S jjjrSU 4S jjr\S\4S	 j5       rS
rU =r$ )r      a  
Creates a Chi-squared distribution parameterized by shape parameter :attr:`df`.
This is exactly equivalent to ``Gamma(alpha=0.5*df, beta=0.5)``

Example::

    >>> # xdoctest: +IGNORE_WANT("non-deterministic")
    >>> m = Chi2(torch.tensor([1.0]))
    >>> m.sample()  # Chi2 distributed with shape df=1
    tensor([ 0.1046])

Args:
    df (float or Tensor): shape parameter of the distribution
dfNvalidate_argsreturnc                 *   > [         TU ]  SU-  SUS9  g )Ng      ?)r
   )super__init__)selfr	   r
   	__class__s      R/home/james-whalen/.local/lib/python3.13/site-packages/torch/distributions/chi2.pyr   Chi2.__init__   s    
 	r3mD    c                 N   > U R                  [        U5      n[        TU ]  X5      $ N)_get_checked_instancer   r   expand)r   batch_shape	_instancenewr   s       r   r   Chi2.expand$   s$    ((y9w~k//r   c                      U R                   S-  $ )N   )concentration)r   s    r   r	   Chi2.df(   s    !!A%%r    r   )__name__
__module____qualname____firstlineno____doc__r   positivearg_constraintsr   floatboolr   r   propertyr	   __static_attributes____classcell__)r   s   @r   r   r      sr     [112O
 &*EUNE d{E 
	E E0 &F & &r   N)torchr   torch.distributionsr   torch.distributions.gammar   __all__r   r    r   r   <module>r1      s%     + + (&5 &r   