diff --git a/mindspore/nn/probability/bijector/bijector.py b/mindspore/nn/probability/bijector/bijector.py
index 22777231f65ba54a933d257d350e57a145232341..e6530b9f7026d7cc1acf8ad3b30ae5ba053de8e2 100644
--- a/mindspore/nn/probability/bijector/bijector.py
+++ b/mindspore/nn/probability/bijector/bijector.py
@@ -69,31 +69,31 @@ class Bijector(Cell):
     def is_injective(self):
         return self._is_injective
 
-    def forward(self, *args):
+    def forward(self, *args, **kwargs):
         """
         Forward transformation: transform the input value to another distribution.
         """
-        return self._forward(*args)
+        return self._forward(*args, **kwargs)
 
-    def inverse(self, *args):
+    def inverse(self, *args, **kwargs):
         """
         Inverse transformation: transform the input value back to the original distribution.
         """
-        return self._inverse(*args)
+        return self._inverse(*args, **kwargs)
 
-    def forward_log_jacobian(self, *args):
+    def forward_log_jacobian(self, *args, **kwargs):
         """
         Logarithm of the derivative of forward transformation.
         """
-        return self._forward_log_jacobian(*args)
+        return self._forward_log_jacobian(*args, **kwargs)
 
-    def inverse_log_jacobian(self, *args):
+    def inverse_log_jacobian(self, *args, **kwargs):
         """
         Logarithm of the derivative of forward transformation.
         """
-        return self._inverse_log_jacobian(*args)
+        return self._inverse_log_jacobian(*args, **kwargs)
 
-    def __call__(self, *args):
+    def __call__(self, *args, **kwargs):
         """
         Call Bijector directly.
         This __call__ may go into two directions:
@@ -107,9 +107,9 @@ class Bijector(Cell):
         """
         if isinstance(args[0], Distribution):
             return TransformedDistribution(self, args[0])
-        return super(Bijector, self).__call__(*args)
+        return super(Bijector, self).__call__(*args, **kwargs)
 
-    def construct(self, name, *args):
+    def construct(self, name, *args, **kwargs):
         """
         Override construct in Cell.
 
@@ -120,11 +120,11 @@ class Bijector(Cell):
             Always raise RuntimeError as Distribution should not be called directly.
         """
         if name == 'forward':
-            return self.forward(*args)
+            return self.forward(*args, **kwargs)
         if name == 'inverse':
-            return self.inverse(*args)
+            return self.inverse(*args, **kwargs)
         if name == 'forward_log_jacobian':
-            return self.forward_log_jacobian(*args)
+            return self.forward_log_jacobian(*args, **kwargs)
         if name == 'inverse_log_jacobian':
-            return self.inverse_log_jacobian(*args)
+            return self.inverse_log_jacobian(*args, **kwargs)
         return None
diff --git a/mindspore/nn/probability/distribution/distribution.py b/mindspore/nn/probability/distribution/distribution.py
index dd3d39f0d702188a2805f88de5d2a33357bc347f..90ad66292d7e273e5d8f1303c9b69c0b8b29356a 100644
--- a/mindspore/nn/probability/distribution/distribution.py
+++ b/mindspore/nn/probability/distribution/distribution.py
@@ -27,7 +27,7 @@ class Distribution(Cell):
 
     Note:
         Derived class should override operations such as ,_mean, _prob,
-        and _log_prob. Arguments should be passed in through *args.
+        and _log_prob. Arguments should be passed in through *args or **kwargs.
 
         Dist_spec_args are unique for each type of distribution. For example, mean and sd
         are the dist_spec_args for a Normal distribution.
@@ -171,7 +171,7 @@ class Distribution(Cell):
         if hasattr(self, '_cross_entropy'):
             self._call_cross_entropy = self._cross_entropy
 
-    def log_prob(self, *args):
+    def log_prob(self, *args, **kwargs):
         """
         Evaluate the log probability(pdf or pmf) at the given value.
 
@@ -179,18 +179,18 @@ class Distribution(Cell):
             Args must include value.
             Dist_spec_args are optional.
         """
-        return self._call_log_prob(*args)
+        return self._call_log_prob(*args, **kwargs)
 
-    def _calc_prob_from_log_prob(self, *args):
+    def _calc_prob_from_log_prob(self, *args, **kwargs):
         r"""
         Evaluate prob from log probability.
 
         .. math::
             probability(x) = \exp(log_likehood(x))
         """
-        return self.exp(self._log_prob(*args))
+        return self.exp(self._log_prob(*args, **kwargs))
 
-    def prob(self, *args):
+    def prob(self, *args, **kwargs):
         """
         Evaluate the probability (pdf or pmf) at given value.
 
@@ -198,18 +198,18 @@ class Distribution(Cell):
             Args must include value.
             Dist_spec_args are optional.
         """
-        return self._call_prob(*args)
+        return self._call_prob(*args, **kwargs)
 
-    def _calc_log_prob_from_prob(self, *args):
+    def _calc_log_prob_from_prob(self, *args, **kwargs):
         r"""
         Evaluate log probability from probability.
 
         .. math::
             log_prob(x) = \log(prob(x))
         """
-        return self.log(self._prob(*args))
+        return self.log(self._prob(*args, **kwargs))
 
-    def cdf(self, *args):
+    def cdf(self, *args, **kwargs):
         """
         Evaluate the cdf at given value.
 
@@ -217,36 +217,36 @@ class Distribution(Cell):
             Args must include value.
             Dist_spec_args are optional.
         """
-        return self._call_cdf(*args)
+        return self._call_cdf(*args, **kwargs)
 
-    def _calc_cdf_from_log_cdf(self, *args):
+    def _calc_cdf_from_log_cdf(self, *args, **kwargs):
         r"""
         Evaluate cdf from log_cdf.
 
         .. math::
             cdf(x) = \exp(log_cdf(x))
         """
-        return self.exp(self._log_cdf(*args))
+        return self.exp(self._log_cdf(*args, **kwargs))
 
-    def _calc_cdf_from_survival(self, *args):
+    def _calc_cdf_from_survival(self, *args, **kwargs):
         r"""
         Evaluate cdf from survival function.
 
         .. math::
             cdf(x) =  1 - (survival_function(x))
         """
-        return 1.0 - self._survival_function(*args)
+        return 1.0 - self._survival_function(*args, **kwargs)
 
-    def _calc_cdf_from_log_survival(self, *args):
+    def _calc_cdf_from_log_survival(self, *args, **kwargs):
         r"""
         Evaluate cdf from log survival function.
 
         .. math::
             cdf(x) =  1 - (\exp(log_survival(x)))
         """
-        return 1.0 - self.exp(self._log_survival(*args))
+        return 1.0 - self.exp(self._log_survival(*args, **kwargs))
 
-    def log_cdf(self, *args):
+    def log_cdf(self, *args, **kwargs):
         """
         Evaluate the log cdf at given value.
 
@@ -254,18 +254,18 @@ class Distribution(Cell):
             Args must include value.
             Dist_spec_args are optional.
         """
-        return self._call_log_cdf(*args)
+        return self._call_log_cdf(*args, **kwargs)
 
-    def _calc_log_cdf_from_call_cdf(self, *args):
+    def _calc_log_cdf_from_call_cdf(self, *args, **kwargs):
         r"""
         Evaluate log cdf from cdf.
 
         .. math::
             log_cdf(x) = \log(cdf(x))
         """
-        return self.log(self._call_cdf(*args))
+        return self.log(self._call_cdf(*args, **kwargs))
 
-    def survival_function(self, *args):
+    def survival_function(self, *args, **kwargs):
         """
         Evaluate the survival function at given value.
 
@@ -273,27 +273,27 @@ class Distribution(Cell):
             Args must include value.
             Dist_spec_args are optional.
         """
-        return self._call_survival(*args)
+        return self._call_survival(*args, **kwargs)
 
-    def _calc_survival_from_call_cdf(self, *args):
+    def _calc_survival_from_call_cdf(self, *args, **kwargs):
         r"""
         Evaluate survival function from cdf.
 
         .. math::
             survival_function(x) =  1 - (cdf(x))
         """
-        return 1.0 - self._call_cdf(*args)
+        return 1.0 - self._call_cdf(*args, **kwargs)
 
-    def _calc_survival_from_log_survival(self, *args):
+    def _calc_survival_from_log_survival(self, *args, **kwargs):
         r"""
         Evaluate survival function from log survival function.
 
         .. math::
             survival(x) = \exp(survival_function(x))
         """
-        return self.exp(self._log_survival(*args))
+        return self.exp(self._log_survival(*args, **kwargs))
 
-    def log_survival(self, *args):
+    def log_survival(self, *args, **kwargs):
         """
         Evaluate the log survival function at given value.
 
@@ -301,18 +301,18 @@ class Distribution(Cell):
             Args must include value.
             Dist_spec_args are optional.
         """
-        return self._call_log_survival(*args)
+        return self._call_log_survival(*args, **kwargs)
 
-    def _calc_log_survival_from_call_survival(self, *args):
+    def _calc_log_survival_from_call_survival(self, *args, **kwargs):
         r"""
         Evaluate log survival function from survival function.
 
         .. math::
             log_survival(x) = \log(survival_function(x))
         """
-        return self.log(self._call_survival(*args))
+        return self.log(self._call_survival(*args, **kwargs))
 
-    def kl_loss(self, *args):
+    def kl_loss(self, *args, **kwargs):
         """
         Evaluate the KL divergence, i.e. KL(a||b).
 
@@ -320,72 +320,72 @@ class Distribution(Cell):
             Args must include type of the distribution, parameters of distribution b.
             Parameters for distribution a are optional.
         """
-        return self._kl_loss(*args)
+        return self._kl_loss(*args, **kwargs)
 
-    def mean(self, *args):
+    def mean(self, *args, **kwargs):
         """
         Evaluate the mean.
 
         Note:
             Dist_spec_args are optional.
         """
-        return self._mean(*args)
+        return self._mean(*args, **kwargs)
 
-    def mode(self, *args):
+    def mode(self, *args, **kwargs):
         """
         Evaluate the mode.
 
         Note:
             Dist_spec_args are optional.
         """
-        return self._mode(*args)
+        return self._mode(*args, **kwargs)
 
-    def sd(self, *args):
+    def sd(self, *args, **kwargs):
         """
         Evaluate the standard deviation.
 
         Note:
             Dist_spec_args are optional.
         """
-        return self._call_sd(*args)
+        return self._call_sd(*args, **kwargs)
 
-    def var(self, *args):
+    def var(self, *args, **kwargs):
         """
         Evaluate the variance.
 
         Note:
             Dist_spec_args are optional.
         """
-        return self._call_var(*args)
+        return self._call_var(*args, **kwargs)
 
-    def _calc_sd_from_var(self, *args):
+    def _calc_sd_from_var(self, *args, **kwargs):
         r"""
         Evaluate log probability from probability.
 
         .. math::
             STD(x) = \sqrt(VAR(x))
         """
-        return self.sqrt(self._var(*args))
+        return self.sqrt(self._var(*args, **kwargs))
 
-    def _calc_var_from_sd(self, *args):
+    def _calc_var_from_sd(self, *args, **kwargs):
         r"""
         Evaluate log probability from probability.
 
         .. math::
             VAR(x) = STD(x) ^ 2
         """
-        return self.sq(self._sd(*args))
+        return self.sq(self._sd(*args, **kwargs))
 
-    def entropy(self, *args):
+    def entropy(self, *args, **kwargs):
         """
         Evaluate the entropy.
 
         Note:
             Dist_spec_args are optional.
         """
-        return self._entropy(*args)
+        return self._entropy(*args, **kwargs)
 
-    def cross_entropy(self, *args):
+    def cross_entropy(self, *args, **kwargs):
         """
         Evaluate the cross_entropy between distribution a and b.
 
@@ -393,32 +393,29 @@ class Distribution(Cell):
             Args must include type of the distribution, parameters of distribution b.
             Parameters for distribution a are optional.
         """
-        return self._call_cross_entropy(*args)
+        return self._call_cross_entropy(*args, **kwargs)
 
-    def _calc_cross_entropy(self, *args):
+    def _calc_cross_entropy(self, *args, **kwargs):
         r"""
         Evaluate cross_entropy from entropy and kl divergence.
 
         .. math::
             H(X, Y) = H(X) + KL(X||Y)
         """
-        return self._entropy(*args) + self._kl_loss(*args)
+        return self._entropy(*args, **kwargs) + self._kl_loss(*args, **kwargs)
 
-    def sample(self, *args):
+    def sample(self, *args, **kwargs):
         """
         Sampling function.
 
-        Args:
-            *args (list): arguments passed in through construct.
-
         Note:
             Shape of the sample is default to ().
             Dist_spec_args are optional.
         """
-        return self._sample(*args)
+        return self._sample(*args, **kwargs)
 
 
-    def construct(self, name, *args):
+    def construct(self, name, *args, **kwargs):
         """
         Override construct in Cell.
 
@@ -433,31 +430,31 @@ class Distribution(Cell):
         """
 
         if name == 'log_prob':
-            return self._call_log_prob(*args)
+            return self._call_log_prob(*args, **kwargs)
         if name == 'prob':
-            return self._call_prob(*args)
+            return self._call_prob(*args, **kwargs)
         if name == 'cdf':
-            return self._call_cdf(*args)
+            return self._call_cdf(*args, **kwargs)
         if name == 'log_cdf':
-            return self._call_log_cdf(*args)
+            return self._call_log_cdf(*args, **kwargs)
         if name == 'survival_function':
-            return self._call_survival(*args)
+            return self._call_survival(*args, **kwargs)
         if name == 'log_survival':
-            return self._call_log_survival(*args)
+            return self._call_log_survival(*args, **kwargs)
         if name == 'kl_loss':
-            return self._kl_loss(*args)
+            return self._kl_loss(*args, **kwargs)
         if name == 'mean':
-            return self._mean(*args)
+            return self._mean(*args, **kwargs)
         if name == 'mode':
-            return self._mode(*args)
+            return self._mode(*args, **kwargs)
         if name == 'sd':
-            return self._call_sd(*args)
+            return self._call_sd(*args, **kwargs)
         if name == 'var':
-            return self._call_var(*args)
+            return self._call_var(*args, **kwargs)
         if name == 'entropy':
-            return self._entropy(*args)
+            return self._entropy(*args, **kwargs)
         if name == 'cross_entropy':
-            return self._call_cross_entropy(*args)
+            return self._call_cross_entropy(*args, **kwargs)
         if name == 'sample':
-            return self._sample(*args)
+            return self._sample(*args, **kwargs)
         return None
diff --git a/mindspore/nn/probability/distribution/normal.py b/mindspore/nn/probability/distribution/normal.py
index f243a2bc31fc664b4f82ebf00eefe814dbc39c28..a984c161bfd5ed6e06c59b8bb071a1814a179289 100644
--- a/mindspore/nn/probability/distribution/normal.py
+++ b/mindspore/nn/probability/distribution/normal.py
@@ -256,8 +256,5 @@ class Normal(Distribution):
         sd = self._sd_value if sd is None else sd
         batch_shape = self.shape(self.zeroslike(mean) + self.zeroslike(sd))
         sample_shape = shape + batch_shape
-        mean_zero = self.const(0.0)
-        sd_one = self.const(1.0)
-        sample_norm = C.normal(sample_shape, mean_zero, sd_one, self.seed)
-        sample = mean + sample_norm * sd
-        return sample
+        sample_norm = C.normal(sample_shape, mean, sd, self.seed)
+        return sample_norm