fix some errors in docs of probability

mindspore/nn/probability/bijector/invert.py

@@ -43,7 +43,7 @@ class Invert(Bijector):
         ...         return self.invert.forward(x_)
         >>> forward = Net()
         >>> x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
-        >>> ans = forward(Tensor(x, dtype=dtype.float32))
+        >>> ans = forward(Tensor(x, dtype=mindspore.float32))
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/gumbel.py

@@ -52,13 +52,13 @@ class Gumbel(TransformedDistribution):
         >>> class Prob(nn.Cell):
         ...     def __init__(self):
         ...         super(Prob, self).__init__()
-        ...         self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=dtype.float32)
+        ...         self.gum = msd.Gumbel(np.array([0.0]), np.array([[1.0], [2.0]]), dtype=mindspore.float32)
         ...
         ...     def construct(self, x_):
         ...         return self.gum.prob(x_)
         >>> value = np.array([1.0, 2.0]).astype(np.float32)
         >>> pdf = Prob()
-        >>> output = pdf(Tensor(value, dtype=dtype.float32))
+        >>> output = pdf(Tensor(value, dtype=mindspore.float32))
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/log_normal.py

@@ -48,14 +48,14 @@ class LogNormal(msd.TransformedDistribution):
         >>> import mindspore.nn as nn
         >>> import mindspore.nn.probability.distribution as msd
         >>> from mindspore import Tensor
-        ...     class Prob(nn.Cell):
-        ...         def __init__(self):
-        ...             super(Prob, self).__init__()
-        ...             self.ln = msd.LogNormal(np.array([0.3]), np.array([[0.2], [0.4]]), dtype=dtype.float32)
-        ...         def construct(self, x_):
-        ...             return self.ln.prob(x_)
+        >>> class Prob(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Prob, self).__init__()
+        ...         self.ln = msd.LogNormal(np.array([0.3]), np.array([[0.2], [0.4]]), dtype=mindspore.float32)
+        ...     def construct(self, x_):
+        ...         return self.ln.prob(x_)
         >>> pdf = Prob()
-        >>> output = pdf(Tensor([1.0, 2.0], dtype=dtype.float32))
+        >>> output = pdf(Tensor([1.0, 2.0], dtype=mindspore.float32))
     """
 
     def __init__(self,

mindspore/nn/probability/distribution/poisson.py

@@ -35,7 +35,7 @@ class Poisson(Distribution):
         name (str): The name of the distribution. Default: 'Poisson'.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend``
 
     Note:
         `rate` must be strictly greater than 0.
@@ -82,14 +82,14 @@ class Poisson(Distribution):
         >>> # Examples of `mean`, `sd`, `mode`, and `var` are similar.
         >>> ans = p1.mean() # return 2
         >>> print(ans.shape)
-        ()
+        (1,)
         >>> ans = p1.mean(rate_b) # return 1 / rate_b
         >>> print(ans.shape)
         (3,)
         >>> # `rate` must be passed in during function calls.
         >>> ans = p2.mean(rate_a)
         >>> print(ans.shape)
-        ()
+        (1,)
         >>> # Examples of `sample`.
         >>> # Args:
         >>> #     shape (tuple): the shape of the sample. Default: ()

mindspore/nn/probability/distribution/transformed_distribution.py

@@ -58,7 +58,7 @@ class TransformedDistribution(Distribution):
         >>> import mindspore.nn.probability.bijector as msb
         >>> from mindspore import Tensor
         >>> class Net(nn.Cell):
-        ...     def __init__(self, shape, dtype=dtype.float32, seed=0, name='transformed_distribution'):
+        ...     def __init__(self, shape, dtype=mindspore.float32, seed=0, name='transformed_distribution'):
         ...         super(Net, self).__init__()
         ...         # create TransformedDistribution distribution
         ...         self.exp = msb.Exp()
@@ -73,7 +73,7 @@ class TransformedDistribution(Distribution):
         >>> shape = (2, 3)
         >>> net = Net(shape=shape, name="LogNormal")
         >>> x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
-        >>> tx = Tensor(x, dtype=dtype.float32)
+        >>> tx = Tensor(x, dtype=mindspore.float32)
         >>> cdf, sample = net(tx)
     """