parameters: (self,
                 df,
                 mu,
                 sigma,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="StudentT")
docstring: Construct Student's t distributions.
  
      The distributions have degree of freedom `df`, mean `mu`, and scale `sigma`.
  
      The parameters `df`, `mu`, and `sigma` must be shaped in a way that supports
      broadcasting (e.g. `df + mu + sigma` is a valid operation).
  
      Args:
        df: Floating point tensor, the degrees of freedom of the
          distribution(s). `df` must contain only positive values.
        mu: Floating point tensor, the means of the distribution(s).
        sigma: Floating point tensor, the scaling factor for the
          distribution(s). `sigma` must contain only positive values.
          Note that `sigma` is not the standard deviation of this distribution.
        validate_args: `Boolean`, default `False`.  Whether to assert that
          `df > 0` and `sigma > 0`. If `validate_args` is `False` and inputs are
          invalid, correct behavior is not guaranteed.
        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: The name to give Ops created by the initializer.
  
      Raises:
        TypeError: if mu and sigma are different dtypes.
docstring_summary: Construct Student's t distributions.
docstring_tokens: Construct Student s t distributions .
function: def __init__(self,
                 df,
                 mu,
                 sigma,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="StudentT"):
      """Construct Student's t distributions.
  
      The distributions have degree of freedom `df`, mean `mu`, and scale `sigma`.
  
      The parameters `df`, `mu`, and `sigma` must be shaped in a way that supports
      broadcasting (e.g. `df + mu + sigma` is a valid operation).
  
      Args:
        df: Floating point tensor, the degrees of freedom of the
          distribution(s). `df` must contain only positive values.
        mu: Floating point tensor, the means of the distribution(s).
        sigma: Floating point tensor, the scaling factor for the
          distribution(s). `sigma` must contain only positive values.
          Note that `sigma` is not the standard deviation of this distribution.
        validate_args: `Boolean`, default `False`.  Whether to assert that
          `df > 0` and `sigma > 0`. If `validate_args` is `False` and inputs are
          invalid, correct behavior is not guaranteed.
        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: The name to give Ops created by the initializer.
  
      Raises:
        TypeError: if mu and sigma are different dtypes.
      """
      with ops.name_scope(name, values=[df, mu, sigma]) as ns:
        with ops.control_dependencies([
            check_ops.assert_positive(df),
            check_ops.assert_positive(sigma),
        ] if validate_args else []):
          self._df = array_ops.identity(df, name="df")
          self._mu = array_ops.identity(mu, name="mu")
          self._sigma = array_ops.identity(sigma, name="sigma")
          contrib_tensor_util.assert_same_float_dtype(
              (self._df, self._mu, self._sigma))
          super(StudentT, self).__init__(
              dtype=self._sigma.dtype,
              parameters={"df": self._df, "mu": self._mu, "sigma": self._sigma},
              is_continuous=True,
              is_reparameterized=True,
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              name=ns)
function_tokens: def __init__ ( self , df , mu , sigma , validate_args = False , allow_nan_stats = True , name = "StudentT" ) : with ops . name_scope ( name , values = [ df , mu , sigma ] ) as ns : with ops . control_dependencies ( [ check_ops . assert_positive ( df ) , check_ops . assert_positive ( sigma ) , ] if validate_args else [ ] ) : self . _df = array_ops . identity ( df , name = "df" ) self . _mu = array_ops . identity ( mu , name = "mu" ) self . _sigma = array_ops . identity ( sigma , name = "sigma" ) contrib_tensor_util . assert_same_float_dtype ( ( self . _df , self . _mu , self . _sigma ) ) super ( StudentT , self ) . __init__ ( dtype = self . _sigma . dtype , parameters = { "df" : self . _df , "mu" : self . _mu , "sigma" : self . _sigma } , is_continuous = True , is_reparameterized = True , validate_args = validate_args , allow_nan_stats = allow_nan_stats , name = ns )