fix numpy api

mindspore/common/tensor.py

@@ -888,6 +888,9 @@ class Tensor(Tensor_):
                 tensor, unless it has an integer dtype with a precision less than :class:`float32`.
                 In that case, :class:`float32` is used. Default: None.
 
+        Raises:
+            ValueError: if axis is out of range.
+
         Returns:
             Tensor.
 
@@ -979,7 +982,7 @@ class Tensor(Tensor_):
             value. If `axis` is given, the result is an array of dimension ``self.ndim - 1``.
 
         Raises:
-            TypeError: if the input is not a tensor.
+            TypeError: if arguments have types not specified above.
 
         Supported Platforms:
             ``Ascend`` ``GPU`` ``CPU``
@@ -1024,7 +1027,7 @@ class Tensor(Tensor_):
             value. If `axis` is given, the result is an array of dimension ``self.ndim - 1``.
 
         Raises:
-            TypeError: if the input is not a tensor.
+            TypeError: if arguments have types not specified above.
 
         Supported Platforms:
             ``Ascend`` ``GPU`` ``CPU``
@@ -1099,7 +1102,7 @@ class Tensor(Tensor_):
             Tensor.
 
         Raises:
-            TypeError: if the input is not a tensor.
+            TypeError: if `self` is not a tensor, or `axis` and `keepdims` have types not specified above.
 
         Supported Platforms:
             ``Ascend`` ``GPU`` ``CPU``
@@ -1147,6 +1150,10 @@ class Tensor(Tensor_):
             Tensor, a tensor with the elements of input tensor, but where values
             < `xmin` are replaced with `xmin`, and those > `xmax` with `xmax`.
 
+        Raises:
+            TypeError: If inputs have types not specified above.
+            ValueError: If the shapes of `x1` and `x2` cannot broadcast, or both `xmin` and `xmax` are `None`.
+
         Supported Platforms:
             ``Ascend`` ``GPU`` ``CPU``
 
@@ -1477,8 +1484,7 @@ class Tensor(Tensor_):
             Tensor, the indexed result.
 
         Raises:
-            ValueError: if axis is out of range.
-            TypeError: if the input is not a Tensor.
+            ValueError: if `axis` is out of range, or `mode` has values other than (‘raise’, ‘wrap’, ‘clip’)
 
         Supported Platforms:
             ``Ascend`` ``GPU`` ``CPU``
@@ -1824,7 +1830,7 @@ class Tensor(Tensor_):
 
         Raises:
             ValueError: if axis is out of range.
-            TypeError: if input is not a Tensor.
+            TypeError: if arguments have types not specified above.
 
         Supported Platforms:
             ``Ascend`` ``GPU`` ``CPU``

mindspore/numpy/array_creations.py

@@ -1689,7 +1689,7 @@ def vander(x, N=None, increasing=False):
 
     Examples:
         >>> import mindspore.numpy as np
-        >>> print(np.vander([1,2,3,4,5]))
+        >>> print(np.vander([1., 2., 3., 4., 5.]))
         [[  1   1   1   1   1]
          [ 16   8   4   2   1]
          [ 81  27   9   3   1]

mindspore/numpy/math_ops.py

@@ -121,6 +121,10 @@ def count_nonzero(x, axis=None, keepdims=False):
         Tensor, indicating number of non-zero values in the `x` along a given axis.
         Otherwise, the total number of non-zero values in `x` is returned.
 
+    Raises:
+        TypeError: If axis is not int or tuple.
+        ValueError: If axis is not in range [-x.ndim, x.ndim)
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -161,6 +165,10 @@ def clip(x, xmin, xmax, dtype=None):
         Tensor, a tensor with the elements of `x`, but where values
         < `xmin` are replaced with `xmin`, and those > `xmax` with `xmax`.
 
+    Raises:
+        TypeError: If inputs have types not specified above.
+        ValueError: If the shapes of `x1` and `x2` cannot broadcast, or both `xmin` and `xmax` are `None`.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -3577,8 +3585,8 @@ def arctan(x, dtype=None):
     Examples:
         >>> import mindspore.numpy as np
         >>> x = np.arange(5).astype('float32')
-        >>> print(np.tan(x))
-        [ 0.          1.5574077  -2.1850398  -0.14254655  1.1578213 ]
+        >>> print(np.arctan(x))
+        [0.        0.7853982 1.1071488 1.2490457 1.3258177]
     """
     x = _cast_type_for_trigonometric(x)
     return _apply_tensor_op(F.atan, x, dtype=dtype)