Fix distributions with broken slicing caused by not overriding parame…

…ters; as identified by hypothesis testing.

PiperOrigin-RevId: 237472521

tensorflow_probability/python/distributions/chi.py

@@ -78,6 +78,7 @@ def __init__(self,
       name: Python `str` name prefixed to Ops created by this class.
         Default value: `'Chi'`.
     """
+    parameters = dict(locals())
     with tf.compat.v1.name_scope(name, values=[df]) as name:
       df = tf.convert_to_tensor(
           value=df,
@@ -93,7 +94,8 @@ def __init__(self,
                                  validate_args=validate_args,
                                  allow_nan_stats=allow_nan_stats,
                                  name=name),
-          bijector=invert_bijector.Invert(square_bijector.Square()))
+          bijector=invert_bijector.Invert(square_bijector.Square()),
+          parameters=parameters)
 
   def _params_event_ndims(self):
     return dict(df=0)

tensorflow_probability/python/distributions/gumbel.py

@@ -127,6 +127,7 @@ def __init__(self,
     Raises:
       TypeError: if loc and scale are different dtypes.
     """
+    parameters = dict(locals())
     with tf.compat.v1.name_scope(name, values=[loc, scale]) as name:
       dtype = dtype_util.common_dtype([loc, scale], preferred_dtype=tf.float32)
       loc = tf.convert_to_tensor(value=loc, name="loc", dtype=dtype)
@@ -153,6 +154,7 @@ def __init__(self,
           # be inverted.
           bijector=invert_bijector.Invert(self._gumbel_bijector),
           batch_shape=distribution_util.get_broadcast_shape(loc, scale),
+          parameters=parameters,
           name=name)
 
   @staticmethod

tensorflow_probability/python/distributions/inverse_gamma.py

@@ -162,8 +162,8 @@ def __init__(self,
       with tf.control_dependencies([
           tf.compat.v1.assert_positive(
               concentration, message="Concentration must be positive."),
-          tf.compat.v1
-          .assert_positive(scale, message="Scale must be positive."),
+          tf.compat.v1.assert_positive(
+              scale, message="Scale must be positive."),
       ] if validate_args else []):
         self._concentration = tf.identity(concentration, name="concentration")
         self._scale = tf.identity(scale, name="scale")

tensorflow_probability/python/distributions/kumaraswamy.py

@@ -27,7 +27,6 @@
 from tensorflow_probability.python.distributions import uniform
 from tensorflow_probability.python.internal import distribution_util
 from tensorflow_probability.python.internal import dtype_util
-from tensorflow_probability.python.internal import reparameterization
 
 __all__ = [
     "Kumaraswamy",
@@ -146,6 +145,7 @@ def __init__(self,
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
+    parameters = dict(locals())
     with tf.compat.v1.name_scope(
         name, values=[concentration1, concentration0]) as name:
       dtype = dtype_util.common_dtype([concentration1, concentration0],
@@ -165,8 +165,8 @@ def __init__(self,
             validate_args=validate_args),
         batch_shape=distribution_util.get_broadcast_shape(
             concentration1, concentration0),
+        parameters=parameters,
         name=name)
-    self._reparameterization_type = reparameterization.FULLY_REPARAMETERIZED
 
   def _params_event_ndims(self):
     return dict(concentration1=0, concentration0=0)

tensorflow_probability/python/distributions/lognormal.py

@@ -61,6 +61,7 @@ def __init__(self,
         undefined statistics will return NaN for this statistic.
       name: The name to give Ops created by the initializer.
     """
+    parameters = dict(locals())
     with tf.compat.v1.name_scope(name, values=[loc, scale]) as name:
       dtype = dtype_util.common_dtype([loc, scale], tf.float32)
       super(LogNormal, self).__init__(
@@ -70,6 +71,7 @@ def __init__(self,
                   value=scale, name="scale", dtype=dtype)),
           bijector=exp_bijector.Exp(),
           validate_args=validate_args,
+          parameters=parameters,
           name=name)
 
   def _params_event_ndims(self):

tensorflow_probability/python/distributions/transformed_distribution.py

@@ -227,6 +227,7 @@ def __init__(self,
                batch_shape=None,
                event_shape=None,
                validate_args=False,
+               parameters=None,
                name=None):
     """Construct a Transformed Distribution.
 
@@ -243,10 +244,12 @@ def __init__(self,
         parameters are checked for validity despite possibly degrading runtime
         performance. When `False` invalid inputs may silently render incorrect
         outputs.
+      parameters: Locals dict captured by subclass constructor, to be used for
+        copy/slice re-instantiation operations.
       name: Python `str` name prefixed to Ops created by this class. Default:
         `bijector.name + distribution.name`.
     """
-    parameters = dict(locals())
+    parameters = dict(locals()) if parameters is None else parameters
     name = name or (("" if bijector is None else bijector.name) +
                     distribution.name)
     with tf.compat.v1.name_scope(