fix bugs

mindspore/nn/optim/rmsprop.py

@@ -133,7 +133,7 @@ class RMSProp(Optimizer):
     Examples:
         >>> net = Net()
         >>> #1) All parameters use the same learning rate and weight decay
-        >>> optim = nn.RMSProp(params=net.trainable_params(), learning_rate=lr)
+        >>> optim = nn.RMSProp(params=net.trainable_params(), learning_rate=0.1)
         >>>
         >>> #2) Use parameter groups and set different values
         >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))

mindspore/ops/operations/array_ops.py

@@ -379,7 +379,7 @@ class IsInstance(PrimitiveWithInfer):
 
     Examples:
         >>> a = 1
-        >>> result = P.IsInstance()(a, mindspore.int32)
+        >>> result = P.IsInstance()(a, mindspore.int64)
         >>> print(result)
         True
     """
@@ -1640,7 +1640,7 @@ class Tile(PrimitiveWithInfer):
         x_shp = x['shape']
         validator.check_value_type("multiples", multiples_v, [tuple], self.name)
         for i, multiple in enumerate(multiples_v):
-            validator.check_value_type("multiples[%d]" % i, multiple, [int], self.name)
+            validator.check_positive_int(multiple, "multiples[%d]" % i, self.name)
         validator.check_value_type("x[\'dtype\']", x["dtype"], mstype.tensor_type, self.name)
         len_sub = len(multiples_v) - len(x_shp)
         multiples_w = None

mindspore/ops/operations/nn_ops.py

@@ -3037,11 +3037,12 @@ class Pad(PrimitiveWithInfer):
         >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
         >>> pad_op = P.Pad(((1, 2), (2, 1)))
         >>> output_tensor = pad_op(input_tensor)
-        >>> assert output_tensor == Tensor(np.array([[ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
-        >>>                                          [ 0. ,  0. , -0.1,  0.3,  3.6,  0. ],
-        >>>                                          [ 0. ,  0. ,  0.4,  0.5, -3.2,  0. ],
-        >>>                                          [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ],
-        >>>                                          [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ]]), mindspore.float32)
+        >>> print(output_tensor)
+        [[ 0.   0.   0.   0.   0.   0. ]
+         [ 0.   0.  -0.1  0.3  3.6  0. ]
+         [ 0.   0.   0.4  0.5 -3.2  0. ]
+         [ 0.   0.   0.   0.   0.   0. ]
+         [ 0.   0.   0.   0.   0.   0. ]]
     """
 
     @prim_attr_register
@@ -4857,7 +4858,7 @@ class ApplyPowerSign(PrimitiveWithInfer):
         >>>         self.beta = 0.9
         >>>     def construct(self, grad):
         >>>         out = self.apply_power_sign(self.var, self.m, self.lr, self.logbase,
-        >>>                                        self.sign_decay, self.beta, grad)
+        ...                                        self.sign_decay, self.beta, grad)
         >>>         return out
         >>> net = Net()
         >>> grad = Tensor(np.random.rand(3, 3).astype(np.float32))