parameters: (self,
                 lam,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="Poisson")
docstring: Construct Poisson distributions.
  
      Args:
        lam: Floating point tensor, the rate parameter of the
          distribution(s). `lam` must be positive.
        validate_args: `Boolean`, default `False`.  Whether to assert that
          `lam > 0` as well as inputs to pmf computations are non-negative
          integers. If validate_args is `False`, then `pmf` computations might
          return `NaN`, but can be evaluated at any real value.
        allow_nan_stats: `Boolean`, default `True`.  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: A name for this distribution.
docstring_summary: Construct Poisson distributions.
docstring_tokens: Construct Poisson distributions .
function: def __init__(self,
                 lam,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="Poisson"):
      """Construct Poisson distributions.
  
      Args:
        lam: Floating point tensor, the rate parameter of the
          distribution(s). `lam` must be positive.
        validate_args: `Boolean`, default `False`.  Whether to assert that
          `lam > 0` as well as inputs to pmf computations are non-negative
          integers. If validate_args is `False`, then `pmf` computations might
          return `NaN`, but can be evaluated at any real value.
        allow_nan_stats: `Boolean`, default `True`.  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: A name for this distribution.
      """
      with ops.name_scope(name, values=[lam]) as ns:
        with ops.control_dependencies([check_ops.assert_positive(lam)] if
                                      validate_args else []):
          self._lam = array_ops.identity(lam, name="lam")
          super(Poisson, self).__init__(
              dtype=self._lam.dtype,
              parameters={"lam": self._lam},
              is_continuous=False,
              is_reparameterized=False,
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              name=ns)
function_tokens: def __init__ ( self , lam , validate_args = False , allow_nan_stats = True , name = "Poisson" ) : with ops . name_scope ( name , values = [ lam ] ) as ns : with ops . control_dependencies ( [ check_ops . assert_positive ( lam ) ] if validate_args else [ ] ) : self . _lam = array_ops . identity ( lam , name = "lam" ) super ( Poisson , self ) . __init__ ( dtype = self . _lam . dtype , parameters = { "lam" : self . _lam } , is_continuous = False , is_reparameterized = False , validate_args = validate_args , allow_nan_stats = allow_nan_stats , name = ns )