def __init__(self,<EOL>concentration,<EOL>rate,<EOL>validate_args=False,<EOL>allow_nan_stats=True,<EOL>name="<STR_LIT>"):
parameters = dict(locals())<EOL>with tf.name_scope(name) as name:<EOL><INDENT>dtype = dtype_util.common_dtype([concentration, rate], tf.float32)<EOL>concentration = tf.convert_to_tensor(<EOL>value=concentration, name="<STR_LIT>", dtype=dtype)<EOL>rate = tf.convert_to_tensor(value=rate, name="<STR_LIT>", dtype=dtype)<EOL>with tf.control_dependencies([<EOL>assert_util.assert_positive(concentration),<EOL>assert_util.assert_positive(rate),<EOL>] if validate_args else []):<EOL><INDENT>self._concentration = tf.identity(concentration)<EOL>self._rate = tf.identity(rate)<EOL>dtype_util.assert_same_float_dtype([self._concentration, self._rate])<EOL><DEDENT><DEDENT>super(Gamma, self).__init__(<EOL>dtype=dtype,<EOL>validate_args=validate_args,<EOL>allow_nan_stats=allow_nan_stats,<EOL>reparameterization_type=reparameterization.FULLY_REPARAMETERIZED,<EOL>parameters=parameters,<EOL>graph_parents=[self._concentration, self._rate],<EOL>name=name)<EOL>
Construct Gamma with `concentration` and `rate` parameters.

        The parameters `concentration` and `rate` must be shaped in a way that
        supports broadcasting (e.g. `concentration + rate` is a valid operation).

        Args:
          concentration: Floating point tensor, the concentration params of the
            distribution(s). Must contain only positive values.
          rate: Floating point tensor, the inverse scale params of the
            distribution(s). Must contain only positive values.
          validate_args: Python `bool`, default `False`. When `True` distribution
            parameters are checked for validity despite possibly degrading runtime
            performance. When `False` invalid inputs may silently render incorrect
            outputs.
          allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
            (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
            result is undefined. When `False`, an exception is raised if one or
            more of the statistic's batch members are undefined.
          name: Python `str` name prefixed to Ops created by this class.

        Raises:
          TypeError: if `concentration` and `rate` are different dtypes.