Fixed some doc issues and removed context checking in CheckTensor

mindspore/nn/probability/bijector/bijector.py

@@ -91,10 +91,8 @@ class Bijector(Cell):
         """
         Check availability of `value` as a Tensor.
         """
-        if self.context_mode == 0:
-            self.checktensor(value, name)
-            return value
-        return self.checktensor(value, name)
+        self.checktensor(value, name)
+        return value
 
     def cast_param_by_value(self, value, para):
         local = self.cast_base(para, self.dtype_base(value))

mindspore/nn/probability/bijector/scalar_affine.py

@@ -65,8 +65,6 @@ class ScalarAffine(Bijector):
             'scale', scale, [int, float], type(self).__name__)
         validator.check_value_type(
             'shift', shift, [int, float], type(self).__name__)
-        self._scale = cast_to_tensor(scale)
-        self._shift = cast_to_tensor(shift)
         super(ScalarAffine, self).__init__(
             is_constant_jacobian=True,
             is_injective=True,
@@ -74,6 +72,9 @@ class ScalarAffine(Bijector):
             dtype=None,
             param=param)
 
+        self._scale = cast_to_tensor(scale)
+        self._shift = cast_to_tensor(shift)
+
         self.abs = P.Abs()
         self.oneslike = P.OnesLike()
         self.dtypeop = P.DType()

mindspore/nn/probability/distribution/bernoulli.py

@@ -273,7 +273,7 @@ class Bernoulli(Distribution):
 
         Args:
             shape (tuple): The shape of the sample. Default: ().
-            probs1 (Tensor, Number): `probs1` of the samples. Default: self.probs.
+            probs1 (Tensor): `probs1` of the samples. Default: self.probs.
 
         Returns:
             Tensor, shape is shape + batch_shape.

mindspore/nn/probability/distribution/categorical.py

@@ -47,7 +47,7 @@ class Categorical(Distribution):
         >>>     def __init__(self, probs):
         >>>         super(net, self).__init__():
         >>>         self.ca = msd.Categorical(probs=[0.2, 0.8], dtype=mstype.int32)
-        >>>         self.ca1 = msd.Categorical(probs=[0.2, 0.8], dtype=mstype.int32)
+        >>>         self.ca1 = msd.Categorical(dtype=mstype.int32)
         >>>
         >>>     # All the following calls in construct are valid
         >>>     def construct(self, value):
@@ -95,8 +95,8 @@ class Categorical(Distribution):
         >>>         #     probs (Tensor): event probabilities. Default: self.probs.
         >>>         ans = self.ca.sample()
         >>>         ans = self.ca.sample((2,3))
-        >>>         ans = self.b1.sample((2,3), probs_b)
-        >>>         ans = self.b2.sample((2,3), probs_a)
+        >>>         ans = self.ca.sample((2,3), probs_b)
+        >>>         ans = self.ca1.sample((2,3), probs_a)
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/distribution.py

@@ -165,10 +165,7 @@ class Distribution(Cell):
         for arg, name, default in zip(args, self.parameter_names, self.default_parameters):
             # check if the argument is a Tensor
             if arg is not None:
-                if self.context_mode == 0:
-                    self.checktensor(arg, name)
-                else:
-                    arg = self.checktensor(arg, name)
+                self.checktensor(arg, name)
             else:
                 arg = default if default is not None else raise_none_error(
                     name)
@@ -687,8 +684,8 @@ class Distribution(Cell):
 
         Note:
             Names of supported functions include:
-            'prob', 'log_prob', 'cdf', 'log_cdf', 'survival_function', 'log_survival'
-            'var', 'sd', 'entropy', 'kl_loss', 'cross_entropy', and 'sample'.
+            'prob', 'log_prob', 'cdf', 'log_cdf', 'survival_function', 'log_survival',
+            'var', 'sd', 'mode', 'mean', 'entropy', 'kl_loss', 'cross_entropy', and 'sample'.
 
         Args:
             name (str): The name of the function.

mindspore/nn/probability/distribution/logistic.py

@@ -94,8 +94,8 @@ class Logistic(Distribution):
         >>>         #     scale (Tensor): the scale of the distribution. Default: self.scale.
         >>>         ans = self.l1.sample()
         >>>         ans = self.l1.sample((2,3))
-        >>>         ans = self.l1.sample((2,3), scale_b, scale_b)
-        >>>         ans = self.l2.sample((2,3), scale_a, scale_a)
+        >>>         ans = self.l1.sample((2,3), loc_b, scale_b)
+        >>>         ans = self.l2.sample((2,3), loc_a, scale_a)
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/transformed_distribution.py

@@ -31,7 +31,7 @@ class TransformedDistribution(Distribution):
     Args:
         bijector (Bijector): The transformation to perform.
         distribution (Distribution): The original distribution.
-        seed (int): The seed is used in sampling. The global seed is used if it is None.
+        seed (int): The seed is used in sampling. The global seed is used if it is None. Default:None.
           If this seed is given when a TransformedDistribution object is initialised, the object's sampling function
           will use this seed; elsewise, the underlying distribution's seed will be used.
         name (str): The name of the transformed distribution. Default: 'transformed_distribution'.