From 16b5e21ef4be2ace560b1c5308dd08a298603594 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 25 May 2018 21:38:56 -0700
Subject: [PATCH] Use dict(locals()) instead of
 distribution_util.parent_frame_arguments. This will be much faster at object
 construction time (going forward we'll figure out a way to make this a
 function to call).

PiperOrigin-RevId: 198141184
---
 tensorflow/contrib/distributions/python/ops/autoregressive.py       | 2 +-
 tensorflow/contrib/distributions/python/ops/batch_reshape.py        | 3 +--
 tensorflow/contrib/distributions/python/ops/binomial.py             | 2 +-
 tensorflow/contrib/distributions/python/ops/cauchy.py               | 3 +--
 tensorflow/contrib/distributions/python/ops/chi2.py                 | 5 ++---
 tensorflow/contrib/distributions/python/ops/deterministic.py        | 3 +--
 tensorflow/contrib/distributions/python/ops/geometric.py            | 2 +-
 tensorflow/contrib/distributions/python/ops/gumbel.py               | 3 +--
 tensorflow/contrib/distributions/python/ops/half_normal.py          | 3 +--
 tensorflow/contrib/distributions/python/ops/independent.py          | 3 +--
 tensorflow/contrib/distributions/python/ops/inverse_gamma.py        | 4 ++--
 tensorflow/contrib/distributions/python/ops/logistic.py             | 3 +--
 tensorflow/contrib/distributions/python/ops/mixture.py              | 2 +-
 tensorflow/contrib/distributions/python/ops/mixture_same_family.py  | 2 +-
 tensorflow/contrib/distributions/python/ops/mvn_diag.py             | 4 ++--
 .../contrib/distributions/python/ops/mvn_diag_plus_low_rank.py      | 2 +-
 tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py  | 3 +--
 tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py  | 2 +-
 tensorflow/contrib/distributions/python/ops/mvn_tril.py             | 2 +-
 tensorflow/contrib/distributions/python/ops/negative_binomial.py    | 2 +-
 tensorflow/contrib/distributions/python/ops/onehot_categorical.py   | 2 +-
 tensorflow/contrib/distributions/python/ops/poisson.py              | 2 +-
 tensorflow/contrib/distributions/python/ops/poisson_lognormal.py    | 2 +-
 .../contrib/distributions/python/ops/quantized_distribution.py      | 2 +-
 tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py    | 2 +-
 .../contrib/distributions/python/ops/relaxed_onehot_categorical.py  | 2 +-
 tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py         | 2 +-
 tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py | 2 +-
 .../contrib/distributions/python/ops/vector_exponential_diag.py     | 2 +-
 .../distributions/python/ops/vector_exponential_linear_operator.py  | 2 +-
 tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py  | 2 +-
 .../distributions/python/ops/vector_laplace_linear_operator.py      | 2 +-
 .../contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py    | 2 +-
 tensorflow/contrib/distributions/python/ops/vector_student_t.py     | 2 +-
 tensorflow/contrib/distributions/python/ops/wishart.py              | 6 +++---
 tensorflow/python/ops/distributions/bernoulli.py                    | 2 +-
 tensorflow/python/ops/distributions/beta.py                         | 4 ++--
 tensorflow/python/ops/distributions/categorical.py                  | 2 +-
 tensorflow/python/ops/distributions/dirichlet.py                    | 2 +-
 tensorflow/python/ops/distributions/dirichlet_multinomial.py        | 2 +-
 tensorflow/python/ops/distributions/distribution.py                 | 2 +-
 tensorflow/python/ops/distributions/exponential.py                  | 5 ++---
 tensorflow/python/ops/distributions/gamma.py                        | 4 ++--
 tensorflow/python/ops/distributions/laplace.py                      | 5 ++---
 tensorflow/python/ops/distributions/multinomial.py                  | 2 +-
 tensorflow/python/ops/distributions/normal.py                       | 5 ++---
 tensorflow/python/ops/distributions/student_t.py                    | 4 ++--
 tensorflow/python/ops/distributions/transformed_distribution.py     | 2 +-
 tensorflow/python/ops/distributions/uniform.py                      | 3 +--
 49 files changed, 60 insertions(+), 73 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/ops/autoregressive.py b/tensorflow/contrib/distributions/python/ops/autoregressive.py
index d813831..11ca90c 100644
--- a/tensorflow/contrib/distributions/python/ops/autoregressive.py
+++ b/tensorflow/contrib/distributions/python/ops/autoregressive.py
@@ -144,7 +144,7 @@ class Autoregressive(distribution_lib.Distribution):
         `distribution_fn(sample0).event_shape.num_elements()` are both `None`.
       ValueError: if `num_steps < 1`.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name) as name:
       self._distribution_fn = distribution_fn
       self._sample0 = sample0
diff --git a/tensorflow/contrib/distributions/python/ops/batch_reshape.py b/tensorflow/contrib/distributions/python/ops/batch_reshape.py
index c709318..4714caa 100644
--- a/tensorflow/contrib/distributions/python/ops/batch_reshape.py
+++ b/tensorflow/contrib/distributions/python/ops/batch_reshape.py
@@ -28,7 +28,6 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import distribution as distribution_lib
-from tensorflow.python.ops.distributions import util as distribution_util
 
 
 __all__ = [
@@ -103,7 +102,7 @@ class BatchReshape(distribution_lib.Distribution):
       ValueError: if `batch_shape` size is not the same as a
         `distribution.batch_shape` size.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     name = name or "BatchReshape" + distribution.name
     with ops.name_scope(name, values=[batch_shape]) as name:
       # The unexpanded batch shape may contain up to one dimension of -1.
diff --git a/tensorflow/contrib/distributions/python/ops/binomial.py b/tensorflow/contrib/distributions/python/ops/binomial.py
index 24b26bf..e4944be 100644
--- a/tensorflow/contrib/distributions/python/ops/binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/binomial.py
@@ -163,7 +163,7 @@ class Binomial(distribution.Distribution):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[total_count, logits, probs]) as name:
       self._total_count = self._maybe_assert_valid_total_count(
           ops.convert_to_tensor(total_count, name="total_count"),
diff --git a/tensorflow/contrib/distributions/python/ops/cauchy.py b/tensorflow/contrib/distributions/python/ops/cauchy.py
index f5ffdd8..23b6a83 100644
--- a/tensorflow/contrib/distributions/python/ops/cauchy.py
+++ b/tensorflow/contrib/distributions/python/ops/cauchy.py
@@ -29,7 +29,6 @@ from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
-from tensorflow.python.ops.distributions import util as distribution_util
 
 __all__ = [
     "Cauchy",
@@ -121,7 +120,7 @@ class Cauchy(distribution.Distribution):
     Raises:
       TypeError: if `loc` and `scale` have different `dtype`.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)]
                                     if validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/chi2.py b/tensorflow/contrib/distributions/python/ops/chi2.py
index 08cdc15..686ae1b 100644
--- a/tensorflow/contrib/distributions/python/ops/chi2.py
+++ b/tensorflow/contrib/distributions/python/ops/chi2.py
@@ -25,7 +25,6 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import gamma
-from tensorflow.python.ops.distributions import util as distribution_util
 
 
 __all__ = [
@@ -84,7 +83,7 @@ class Chi2(gamma.Gamma):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     # 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=True
@@ -120,7 +119,7 @@ class Chi2WithAbsDf(Chi2):
                validate_args=False,
                allow_nan_stats=True,
                name="Chi2WithAbsDf"):
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[df]) as name:
       super(Chi2WithAbsDf, self).__init__(
           df=math_ops.floor(
diff --git a/tensorflow/contrib/distributions/python/ops/deterministic.py b/tensorflow/contrib/distributions/python/ops/deterministic.py
index 6d7d6d3..c44c76a 100644
--- a/tensorflow/contrib/distributions/python/ops/deterministic.py
+++ b/tensorflow/contrib/distributions/python/ops/deterministic.py
@@ -32,7 +32,6 @@ from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import distribution
-from tensorflow.python.ops.distributions import util as distribution_util
 
 __all__ = [
     "Deterministic",
@@ -87,7 +86,7 @@ class _BaseDeterministic(distribution.Distribution):
     Raises:
       ValueError:  If `loc` is a scalar.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[loc, atol, rtol]) as name:
       loc = ops.convert_to_tensor(loc, name="loc")
       if is_vector and validate_args:
diff --git a/tensorflow/contrib/distributions/python/ops/geometric.py b/tensorflow/contrib/distributions/python/ops/geometric.py
index 446cff6..e1e42ee 100644
--- a/tensorflow/contrib/distributions/python/ops/geometric.py
+++ b/tensorflow/contrib/distributions/python/ops/geometric.py
@@ -85,7 +85,7 @@ class Geometric(distribution.Distribution):
       name: Python `str` name prefixed to Ops created by this class.
     """
 
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[logits, probs]) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits, probs, validate_args=validate_args, name=name)
diff --git a/tensorflow/contrib/distributions/python/ops/gumbel.py b/tensorflow/contrib/distributions/python/ops/gumbel.py
index ed9ea6f..9d94fd1 100644
--- a/tensorflow/contrib/distributions/python/ops/gumbel.py
+++ b/tensorflow/contrib/distributions/python/ops/gumbel.py
@@ -29,7 +29,6 @@ from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
-from tensorflow.python.ops.distributions import util as distribution_util
 
 
 class _Gumbel(distribution.Distribution):
@@ -125,7 +124,7 @@ class _Gumbel(distribution.Distribution):
     Raises:
       TypeError: if loc and scale are different dtypes.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/half_normal.py b/tensorflow/contrib/distributions/python/ops/half_normal.py
index 7e12767..9c96254 100644
--- a/tensorflow/contrib/distributions/python/ops/half_normal.py
+++ b/tensorflow/contrib/distributions/python/ops/half_normal.py
@@ -31,7 +31,6 @@ from tensorflow.python.ops import nn
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
 from tensorflow.python.ops.distributions import special_math
-from tensorflow.python.ops.distributions import util as distribution_util
 
 
 __all__ = [
@@ -106,7 +105,7 @@ class HalfNormal(distribution.Distribution):
         if one or more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/independent.py b/tensorflow/contrib/distributions/python/ops/independent.py
index fa89fff..cd6eaa8 100644
--- a/tensorflow/contrib/distributions/python/ops/independent.py
+++ b/tensorflow/contrib/distributions/python/ops/independent.py
@@ -29,7 +29,6 @@ from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import distribution as distribution_lib
 from tensorflow.python.ops.distributions import kullback_leibler
-from tensorflow.python.ops.distributions import util as distribution_util
 
 
 class Independent(distribution_lib.Distribution):
@@ -117,7 +116,7 @@ class Independent(distribution_lib.Distribution):
       ValueError: if `reinterpreted_batch_ndims` exceeds
         `distribution.batch_ndims`
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     name = name or "Independent" + distribution.name
     self._distribution = distribution
     with ops.name_scope(name) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
index 85e8e10..208057b 100644
--- a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
+++ b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
@@ -125,7 +125,7 @@ class InverseGamma(distribution.Distribution):
     Raises:
       TypeError: if `concentration` and `rate` are different dtypes.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[concentration, rate]) as name:
       with ops.control_dependencies([
           check_ops.assert_positive(concentration),
@@ -280,7 +280,7 @@ class InverseGammaWithSoftplusConcentrationRate(InverseGamma):
                validate_args=False,
                allow_nan_stats=True,
                name="InverseGammaWithSoftplusConcentrationRate"):
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[concentration, rate]) as name:
       super(InverseGammaWithSoftplusConcentrationRate, self).__init__(
           concentration=nn.softplus(concentration,
diff --git a/tensorflow/contrib/distributions/python/ops/logistic.py b/tensorflow/contrib/distributions/python/ops/logistic.py
index 0103283..27aa863 100644
--- a/tensorflow/contrib/distributions/python/ops/logistic.py
+++ b/tensorflow/contrib/distributions/python/ops/logistic.py
@@ -31,7 +31,6 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
-from tensorflow.python.ops.distributions import util as distribution_util
 
 
 class Logistic(distribution.Distribution):
@@ -120,7 +119,7 @@ class Logistic(distribution.Distribution):
     Raises:
       TypeError: if loc and scale are different dtypes.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/mixture.py b/tensorflow/contrib/distributions/python/ops/mixture.py
index d54f30d..bfb53a0 100644
--- a/tensorflow/contrib/distributions/python/ops/mixture.py
+++ b/tensorflow/contrib/distributions/python/ops/mixture.py
@@ -116,7 +116,7 @@ class Mixture(distribution.Distribution):
         matching static batch shapes, or all components do not
         have matching static event shapes.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     if not isinstance(cat, categorical.Categorical):
       raise TypeError("cat must be a Categorical distribution, but saw: %s" %
                       cat)
diff --git a/tensorflow/contrib/distributions/python/ops/mixture_same_family.py b/tensorflow/contrib/distributions/python/ops/mixture_same_family.py
index c7c90cf..112eefd 100644
--- a/tensorflow/contrib/distributions/python/ops/mixture_same_family.py
+++ b/tensorflow/contrib/distributions/python/ops/mixture_same_family.py
@@ -130,7 +130,7 @@ class MixtureSameFamily(distribution.Distribution):
       ValueError: if `mixture_distribution` categories does not equal
         `components_distribution` rightmost batch shape.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name) as name:
       self._mixture_distribution = mixture_distribution
       self._components_distribution = components_distribution
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_diag.py b/tensorflow/contrib/distributions/python/ops/mvn_diag.py
index cad3985..d2beb2a 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_diag.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_diag.py
@@ -193,7 +193,7 @@ class MultivariateNormalDiag(
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name) as name:
       with ops.name_scope("init", values=[
           loc, scale_diag, scale_identity_multiplier]):
@@ -224,7 +224,7 @@ class MultivariateNormalDiagWithSoftplusScale(MultivariateNormalDiag):
                validate_args=False,
                allow_nan_stats=True,
                name="MultivariateNormalDiagWithSoftplusScale"):
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[scale_diag]) as name:
       super(MultivariateNormalDiagWithSoftplusScale, self).__init__(
           loc=loc,
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py b/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py
index 1c11594..5117379 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_diag_plus_low_rank.py
@@ -215,7 +215,7 @@ class MultivariateNormalDiagPlusLowRank(
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     def _convert_to_tensor(x, name):
       return None if x is None else ops.convert_to_tensor(x, name=name)
     with ops.name_scope(name) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py b/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py
index 47d7d13..57f47db 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_full_covariance.py
@@ -24,7 +24,6 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import linalg_ops
-from tensorflow.python.ops.distributions import util as distribution_util
 
 
 __all__ = [
@@ -156,7 +155,7 @@ class MultivariateNormalFullCovariance(mvn_tril.MultivariateNormalTriL):
     Raises:
       ValueError: if neither `loc` nor `covariance_matrix` are specified.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
 
     # Convert the covariance_matrix up to a scale_tril and call MVNTriL.
     with ops.name_scope(name) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py b/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py
index 79916fe..6a0383d 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py
@@ -170,7 +170,7 @@ class MultivariateNormalLinearOperator(
       ValueError: if `scale` is unspecified.
       TypeError: if not `scale.dtype.is_floating`
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     if scale is None:
       raise ValueError("Missing required `scale` parameter.")
     if not scale.dtype.is_floating:
diff --git a/tensorflow/contrib/distributions/python/ops/mvn_tril.py b/tensorflow/contrib/distributions/python/ops/mvn_tril.py
index d6b0ed9..c809ef3 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn_tril.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn_tril.py
@@ -179,7 +179,7 @@ class MultivariateNormalTriL(
     Raises:
       ValueError: if neither `loc` nor `scale_tril` are specified.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     def _convert_to_tensor(x, name):
       return None if x is None else ops.convert_to_tensor(x, name=name)
     if loc is None and scale_tril is None:
diff --git a/tensorflow/contrib/distributions/python/ops/negative_binomial.py b/tensorflow/contrib/distributions/python/ops/negative_binomial.py
index 1085c56..2bd11e2 100644
--- a/tensorflow/contrib/distributions/python/ops/negative_binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/negative_binomial.py
@@ -90,7 +90,7 @@ class NegativeBinomial(distribution.Distribution):
       name: Python `str` name prefixed to Ops created by this class.
     """
 
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[total_count, logits, probs]) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits, probs, validate_args=validate_args, name=name)
diff --git a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py
index a4b9f3b..3e44c10 100644
--- a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py
+++ b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py
@@ -115,7 +115,7 @@ class OneHotCategorical(distribution.Distribution):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[logits, probs]) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           name=name, logits=logits, probs=probs, validate_args=validate_args,
diff --git a/tensorflow/contrib/distributions/python/ops/poisson.py b/tensorflow/contrib/distributions/python/ops/poisson.py
index b345394..04de810 100644
--- a/tensorflow/contrib/distributions/python/ops/poisson.py
+++ b/tensorflow/contrib/distributions/python/ops/poisson.py
@@ -93,7 +93,7 @@ class Poisson(distribution.Distribution):
       TypeError: if `rate` is not a float-type.
       TypeError: if `log_rate` is not a float-type.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[rate]) as name:
       if (rate is None) == (log_rate is None):
         raise ValueError("Must specify exactly one of `rate` and `log_rate`.")
diff --git a/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py b/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py
index fe72091..7b10ba9 100644
--- a/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py
+++ b/tensorflow/contrib/distributions/python/ops/poisson_lognormal.py
@@ -255,7 +255,7 @@ class PoissonLogNormalQuadratureCompound(distribution_lib.Distribution):
       TypeError: if `quadrature_grid` and `quadrature_probs` have different base
         `dtype`.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[loc, scale]) as name:
       if loc is not None:
         loc = ops.convert_to_tensor(loc, name="loc")
diff --git a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py
index 584d2c3..5ac6c34 100644
--- a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py
+++ b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py
@@ -263,7 +263,7 @@ class QuantizedDistribution(distributions.Distribution):
           `Distribution` or continuous.
       NotImplementedError:  If the base distribution does not implement `cdf`.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     values = (
         list(distribution.parameters.values()) +
         [low, high])
diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
index 0362996..4182ca2 100644
--- a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
+++ b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py
@@ -165,7 +165,7 @@ class RelaxedBernoulli(transformed_distribution.TransformedDistribution):
     Raises:
       ValueError: If both `probs` and `logits` are passed, or if neither.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[logits, probs, temperature]) as name:
       with ops.control_dependencies([check_ops.assert_positive(temperature)]
                                     if validate_args else []):
diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py
index 910c430..5414f34 100644
--- a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py
+++ b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py
@@ -162,7 +162,7 @@ class ExpRelaxedOneHotCategorical(distribution.Distribution):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[logits, probs, temperature]) as name:
 
       self._logits, self._probs = distribution_util.get_logits_and_probs(
diff --git a/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py b/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py
index f04dc8d..a764544 100644
--- a/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py
+++ b/tensorflow/contrib/distributions/python/ops/sinh_arcsinh.py
@@ -132,7 +132,7 @@ class SinhArcsinh(transformed_distribution.TransformedDistribution):
         if one or more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
 
     with ops.name_scope(name,
                         values=[loc, scale, skewness, tailweight]) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py b/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py
index cd6d749..8d4914e 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py
@@ -395,7 +395,7 @@ class VectorDiffeomixture(distribution_lib.Distribution):
       ValueError: if `not distribution.is_scalar_batch`.
       ValueError: if `not distribution.is_scalar_event`.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[mix_loc, temperature]) as name:
       if not scale or len(scale) < 2:
         raise ValueError("Must specify list (or list-like object) of scale "
diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
index 3465d66..a75b3f3 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_diag.py
@@ -175,7 +175,7 @@ class VectorExponentialDiag(
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name) as name:
       with ops.name_scope("init", values=[
           loc, scale_diag, scale_identity_multiplier]):
diff --git a/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
index 2c31b01..a7d4c55 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py
@@ -175,7 +175,7 @@ class VectorExponentialLinearOperator(
       ValueError: if `scale` is unspecified.
       TypeError: if not `scale.dtype.is_floating`
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     if scale is None:
       raise ValueError("Missing required `scale` parameter.")
     if not scale.dtype.is_floating:
diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py
index 6a36018..4a53e7a 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_diag.py
@@ -210,7 +210,7 @@ class VectorLaplaceDiag(
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name):
       with ops.name_scope("init", values=[
           loc, scale_diag, scale_identity_multiplier]):
diff --git a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
index 97e5c76..0566e04 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
@@ -191,7 +191,7 @@ class VectorLaplaceLinearOperator(
       ValueError: if `scale` is unspecified.
       TypeError: if not `scale.dtype.is_floating`
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     if scale is None:
       raise ValueError("Missing required `scale` parameter.")
     if not scale.dtype.is_floating:
diff --git a/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py b/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py
index ff5ca45..bb33cd0 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py
@@ -163,7 +163,7 @@ class VectorSinhArcsinhDiag(transformed_distribution.TransformedDistribution):
     Raises:
       ValueError: if at most `scale_identity_multiplier` is specified.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
 
     with ops.name_scope(
         name,
diff --git a/tensorflow/contrib/distributions/python/ops/vector_student_t.py b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
index 4742f75..21f84dc 100644
--- a/tensorflow/contrib/distributions/python/ops/vector_student_t.py
+++ b/tensorflow/contrib/distributions/python/ops/vector_student_t.py
@@ -175,7 +175,7 @@ class _VectorStudentT(transformed_distribution.TransformedDistribution):
         if one or more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     graph_parents = [df, loc, scale_identity_multiplier, scale_diag,
                      scale_tril, scale_perturb_factor, scale_perturb_diag]
     with ops.name_scope(name) as name:
diff --git a/tensorflow/contrib/distributions/python/ops/wishart.py b/tensorflow/contrib/distributions/python/ops/wishart.py
index f555867..88d4280 100644
--- a/tensorflow/contrib/distributions/python/ops/wishart.py
+++ b/tensorflow/contrib/distributions/python/ops/wishart.py
@@ -107,7 +107,7 @@ class _WishartLinearOperator(distribution.Distribution):
       ValueError: if df < k, where scale operator event shape is
         `(k, k)`
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     self._cholesky_input_output_matrices = cholesky_input_output_matrices
     with ops.name_scope(name) as name:
       with ops.name_scope("init", values=[df, scale_operator]):
@@ -530,7 +530,7 @@ class WishartCholesky(_WishartLinearOperator):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[scale]) as name:
       with ops.name_scope("init", values=[scale]):
         scale = ops.convert_to_tensor(scale)
@@ -646,7 +646,7 @@ class WishartFull(_WishartLinearOperator):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name) as name:
       with ops.name_scope("init", values=[scale]):
         scale = ops.convert_to_tensor(scale)
diff --git a/tensorflow/python/ops/distributions/bernoulli.py b/tensorflow/python/ops/distributions/bernoulli.py
index d7fb3f1..84d9d40 100644
--- a/tensorflow/python/ops/distributions/bernoulli.py
+++ b/tensorflow/python/ops/distributions/bernoulli.py
@@ -71,7 +71,7 @@ class Bernoulli(distribution.Distribution):
     Raises:
       ValueError: If p and logits are passed, or if neither are passed.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits=logits,
diff --git a/tensorflow/python/ops/distributions/beta.py b/tensorflow/python/ops/distributions/beta.py
index b697848..f28f76b 100644
--- a/tensorflow/python/ops/distributions/beta.py
+++ b/tensorflow/python/ops/distributions/beta.py
@@ -150,7 +150,7 @@ class Beta(distribution.Distribution):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[concentration1, concentration0]) as name:
       self._concentration1 = self._maybe_assert_valid_concentration(
           ops.convert_to_tensor(concentration1, name="concentration1"),
@@ -321,7 +321,7 @@ class BetaWithSoftplusConcentration(Beta):
                validate_args=False,
                allow_nan_stats=True,
                name="BetaWithSoftplusConcentration"):
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[concentration1,
                                       concentration0]) as name:
       super(BetaWithSoftplusConcentration, self).__init__(
diff --git a/tensorflow/python/ops/distributions/categorical.py b/tensorflow/python/ops/distributions/categorical.py
index bbdc8c4..b88a051 100644
--- a/tensorflow/python/ops/distributions/categorical.py
+++ b/tensorflow/python/ops/distributions/categorical.py
@@ -182,7 +182,7 @@ class Categorical(distribution.Distribution):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[logits, probs]) as name:
       self._logits, self._probs = distribution_util.get_logits_and_probs(
           logits=logits,
diff --git a/tensorflow/python/ops/distributions/dirichlet.py b/tensorflow/python/ops/distributions/dirichlet.py
index 8d0d1d8..1ab58c1 100644
--- a/tensorflow/python/ops/distributions/dirichlet.py
+++ b/tensorflow/python/ops/distributions/dirichlet.py
@@ -154,7 +154,7 @@ class Dirichlet(distribution.Distribution):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[concentration]) as name:
       self._concentration = self._maybe_assert_valid_concentration(
           ops.convert_to_tensor(concentration, name="concentration"),
diff --git a/tensorflow/python/ops/distributions/dirichlet_multinomial.py b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
index 3a35e0c..5350c82 100644
--- a/tensorflow/python/ops/distributions/dirichlet_multinomial.py
+++ b/tensorflow/python/ops/distributions/dirichlet_multinomial.py
@@ -191,7 +191,7 @@ class DirichletMultinomial(distribution.Distribution):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[total_count, concentration]) as name:
       # Broadcasting works because:
       # * The broadcasting convention is to prepend dimensions of size [1], and
diff --git a/tensorflow/python/ops/distributions/distribution.py b/tensorflow/python/ops/distributions/distribution.py
index a6579e3..0db4749 100644
--- a/tensorflow/python/ops/distributions/distribution.py
+++ b/tensorflow/python/ops/distributions/distribution.py
@@ -525,7 +525,7 @@ class Distribution(_BaseDistribution):
     """Dictionary of parameters used to instantiate this `Distribution`."""
     # Remove "self", "__class__", or other special variables. These can appear
     # if the subclass used:
-    # `parameters = distribution_util.parent_frame_arguments()`.
+    # `parameters = dict(locals())`.
     return dict((k, v) for k, v in self._parameters.items()
                 if not k.startswith("__") and k != "self")
 
diff --git a/tensorflow/python/ops/distributions/exponential.py b/tensorflow/python/ops/distributions/exponential.py
index 1e08f48..24bc3f3 100644
--- a/tensorflow/python/ops/distributions/exponential.py
+++ b/tensorflow/python/ops/distributions/exponential.py
@@ -27,7 +27,6 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import gamma
-from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -91,7 +90,7 @@ class Exponential(gamma.Gamma):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     # Even though all statistics of are defined for valid inputs, this is not
     # true in the parent class "Gamma."  Therefore, passing
     # allow_nan_stats=True
@@ -144,7 +143,7 @@ class ExponentialWithSoftplusRate(Exponential):
                validate_args=False,
                allow_nan_stats=True,
                name="ExponentialWithSoftplusRate"):
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[rate]) as name:
       super(ExponentialWithSoftplusRate, self).__init__(
           rate=nn.softplus(rate, name="softplus_rate"),
diff --git a/tensorflow/python/ops/distributions/gamma.py b/tensorflow/python/ops/distributions/gamma.py
index 7ca690d..163a27f 100644
--- a/tensorflow/python/ops/distributions/gamma.py
+++ b/tensorflow/python/ops/distributions/gamma.py
@@ -126,7 +126,7 @@ class Gamma(distribution.Distribution):
     Raises:
       TypeError: if `concentration` and `rate` are different dtypes.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[concentration, rate]) as name:
       with ops.control_dependencies([
           check_ops.assert_positive(concentration),
@@ -261,7 +261,7 @@ class GammaWithSoftplusConcentrationRate(Gamma):
                validate_args=False,
                allow_nan_stats=True,
                name="GammaWithSoftplusConcentrationRate"):
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[concentration, rate]) as name:
       super(GammaWithSoftplusConcentrationRate, self).__init__(
           concentration=nn.softplus(concentration,
diff --git a/tensorflow/python/ops/distributions/laplace.py b/tensorflow/python/ops/distributions/laplace.py
index ee3a6a4..be17cf2 100644
--- a/tensorflow/python/ops/distributions/laplace.py
+++ b/tensorflow/python/ops/distributions/laplace.py
@@ -33,7 +33,6 @@ from tensorflow.python.ops import nn
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
 from tensorflow.python.ops.distributions import special_math
-from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -101,7 +100,7 @@ class Laplace(distribution.Distribution):
     Raises:
       TypeError: if `loc` and `scale` are of different dtype.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
@@ -218,7 +217,7 @@ class LaplaceWithSoftplusScale(Laplace):
                validate_args=False,
                allow_nan_stats=True,
                name="LaplaceWithSoftplusScale"):
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[loc, scale]) as name:
       super(LaplaceWithSoftplusScale, self).__init__(
           loc=loc,
diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py
index 036ba45..d0943e8 100644
--- a/tensorflow/python/ops/distributions/multinomial.py
+++ b/tensorflow/python/ops/distributions/multinomial.py
@@ -182,7 +182,7 @@ class Multinomial(distribution.Distribution):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[total_count, logits, probs]) as name:
       self._total_count = ops.convert_to_tensor(total_count, name="total_count")
       if validate_args:
diff --git a/tensorflow/python/ops/distributions/normal.py b/tensorflow/python/ops/distributions/normal.py
index 0620aae..d0a987b 100644
--- a/tensorflow/python/ops/distributions/normal.py
+++ b/tensorflow/python/ops/distributions/normal.py
@@ -32,7 +32,6 @@ from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
 from tensorflow.python.ops.distributions import kullback_leibler
 from tensorflow.python.ops.distributions import special_math
-from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -132,7 +131,7 @@ class Normal(distribution.Distribution):
     Raises:
       TypeError: if `loc` and `scale` have different `dtype`.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
@@ -244,7 +243,7 @@ class NormalWithSoftplusScale(Normal):
                validate_args=False,
                allow_nan_stats=True,
                name="NormalWithSoftplusScale"):
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[scale]) as name:
       super(NormalWithSoftplusScale, self).__init__(
           loc=loc,
diff --git a/tensorflow/python/ops/distributions/student_t.py b/tensorflow/python/ops/distributions/student_t.py
index 9330b93..20a2d16 100644
--- a/tensorflow/python/ops/distributions/student_t.py
+++ b/tensorflow/python/ops/distributions/student_t.py
@@ -157,7 +157,7 @@ class StudentT(distribution.Distribution):
     Raises:
       TypeError: if loc and scale are different dtypes.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[df, loc, scale]) as name:
       with ops.control_dependencies([check_ops.assert_positive(df)]
                                     if validate_args else []):
@@ -349,7 +349,7 @@ class StudentTWithAbsDfSoftplusScale(StudentT):
                validate_args=False,
                allow_nan_stats=True,
                name="StudentTWithAbsDfSoftplusScale"):
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[df, scale]) as name:
       super(StudentTWithAbsDfSoftplusScale, self).__init__(
           df=math_ops.floor(math_ops.abs(df)),
diff --git a/tensorflow/python/ops/distributions/transformed_distribution.py b/tensorflow/python/ops/distributions/transformed_distribution.py
index c2674bd..e80bf9e 100644
--- a/tensorflow/python/ops/distributions/transformed_distribution.py
+++ b/tensorflow/python/ops/distributions/transformed_distribution.py
@@ -252,7 +252,7 @@ class TransformedDistribution(distribution_lib.Distribution):
       name: Python `str` name prefixed to Ops created by this class. Default:
         `bijector.name + distribution.name`.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     name = name or (("" if bijector is None else bijector.name) +
                     distribution.name)
     with ops.name_scope(name, values=[event_shape, batch_shape]) as name:
diff --git a/tensorflow/python/ops/distributions/uniform.py b/tensorflow/python/ops/distributions/uniform.py
index dfa1033..e66c4a3 100644
--- a/tensorflow/python/ops/distributions/uniform.py
+++ b/tensorflow/python/ops/distributions/uniform.py
@@ -29,7 +29,6 @@ from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops.distributions import distribution
-from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -103,7 +102,7 @@ class Uniform(distribution.Distribution):
     Raises:
       InvalidArgumentError: if `low >= high` and `validate_args=False`.
     """
-    parameters = distribution_util.parent_frame_arguments()
+    parameters = dict(locals())
     with ops.name_scope(name, values=[low, high]) as name:
       with ops.control_dependencies([
           check_ops.assert_less(
-- 
2.7.4