!10399 fix bugs of op BatchNorm2d, Lamb, LARSUpdate and Depend

From: @lihongkang1
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/nn/layer/normalization.py

@@ -383,10 +383,8 @@ class BatchNorm2d(_BatchNorm):
         >>> print(output)
         [[[[171.99915   46.999763 ]
            [116.99941  191.99904  ]]
-
           [[ 66.999664 250.99875  ]
            [194.99902  102.99948  ]]
-
           [[  8.999955 210.99895  ]
            [ 20.999895 241.9988   ]]]]
     """

mindspore/nn/optim/lamb.py

@@ -250,7 +250,7 @@ class Lamb(Optimizer):
         >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
         >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
         ...                 {'params': no_conv_params, 'lr': poly_decay_lr},
-        ...                 {'order_params': net.trainable_params(0.01, 0.0001, 10, 0.5)}]
+        ...                 {'order_params': net.trainable_params(0.01)}]
         >>> optim = nn.Lamb(group_params, learning_rate=0.1, weight_decay=0.0)
         >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
         >>> # The no_conv_params's parameters will use dynamic learning rate of poly decay learning rate and default

mindspore/ops/operations/nn_ops.py

@@ -5690,18 +5690,19 @@ class LARSUpdate(PrimitiveWithInfer):
         ...         self.lars = ops.LARSUpdate()
         ...         self.reduce = ops.ReduceSum()
         ...     def construct(self, weight, gradient):
-        ...         w_square_sum = self.reduce(ops.Square(weight))
-        ...         grad_square_sum = self.reduce(ops.Square(gradient))
+        ...         w_square_sum = self.reduce(ops.Square()(weight))
+        ...         grad_square_sum = self.reduce(ops.Square()(gradient))
         ...         grad_t = self.lars(weight, gradient, w_square_sum, grad_square_sum, 0.0, 1.0)
         ...         return grad_t
         ...
+        >>> np.random.seed(0)
         >>> weight = np.random.random(size=(2, 3)).astype(np.float32)
         >>> gradient = np.random.random(size=(2, 3)).astype(np.float32)
         >>> net = Net()
         >>> output = net(Tensor(weight), Tensor(gradient))
         >>> print(output)
-        [[1.0630977e-03 1.0647357e-03 1.0038106e-03]
-         [2.9038603e-04 5.9235965e-05 6.8709702e-04]]
+        [[0.00036534 0.00074454 0.00080456]
+         [0.00032014 0.00066101 0.00044157]]
     """
 
     @prim_attr_register

mindspore/ops/operations/other_ops.py

@@ -426,6 +426,9 @@ class Depend(Primitive):
 
     Outputs:
         Tensor, the value passed by last operator.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
     """
 
     @prim_attr_register