parameters: (self,
                 df,
                 validate_args=True,
                 allow_nan_stats=False,
                 name="Chi2")
docstring: Construct Chi2 distributions with parameter `df`.
  
      Args:
        df: Floating point tensor, the degrees of freedom of the
          distribution(s).  `df` must contain only positive values.
        validate_args: Whether to assert that `df > 0`, and that `x > 0` in the
          methods `prob(x)` and `log_prob(x)`. If `validate_args` is `False`
          and the inputs are invalid, correct behavior is not guaranteed.
        allow_nan_stats:  Boolean, default `False`.  If `False`, raise an
          exception if a statistic (e.g. mean/mode/etc...) is undefined for any
          batch member.  If `True`, batch members with valid parameters leading to
          undefined statistics will return NaN for this statistic.
        name: The name to prepend to all ops created by this distribution.
docstring_summary: Construct Chi2 distributions with parameter `df`.
docstring_tokens: Construct Chi2 distributions with parameter df .
function: def __init__(self,
                 df,
                 validate_args=True,
                 allow_nan_stats=False,
                 name="Chi2"):
      """Construct Chi2 distributions with parameter `df`.
  
      Args:
        df: Floating point tensor, the degrees of freedom of the
          distribution(s).  `df` must contain only positive values.
        validate_args: Whether to assert that `df > 0`, and that `x > 0` in the
          methods `prob(x)` and `log_prob(x)`. If `validate_args` is `False`
          and the inputs are invalid, correct behavior is not guaranteed.
        allow_nan_stats:  Boolean, default `False`.  If `False`, raise an
          exception if a statistic (e.g. mean/mode/etc...) is undefined for any
          batch member.  If `True`, batch members with valid parameters leading to
          undefined statistics will return NaN for this statistic.
        name: The name to prepend to all ops created by this distribution.
      """
      # Even though all stats of chi2 are defined for valid parameters, this is
      # not true in the parent class "gamma."  therefore, passing
      # allow_nan_stats=False
      # through to the parent class results in unnecessary asserts.
      with ops.op_scope([df], name):
        df = ops.convert_to_tensor(df)
        self._df = df
        super(Chi2, self).__init__(alpha=df / 2,
                                   beta=constant_op.constant(0.5, dtype=df.dtype),
                                   validate_args=validate_args,
                                   allow_nan_stats=allow_nan_stats)
function_tokens: def __init__ ( self , df , validate_args = True , allow_nan_stats = False , name = "Chi2" ) : # Even though all stats of chi2 are defined for valid parameters, this is # not true in the parent class "gamma."  therefore, passing # allow_nan_stats=False # through to the parent class results in unnecessary asserts. with ops . op_scope ( [ df ] , name ) : df = ops . convert_to_tensor ( df ) self . _df = df super ( Chi2 , self ) . __init__ ( alpha = df / 2 , beta = constant_op . constant ( 0.5 , dtype = df . dtype ) , validate_args = validate_args , allow_nan_stats = allow_nan_stats )