!13456 multdiceloss api

From: @lijiaqi0612
Reviewed-by: @zh_qh,@kisnwang
Signed-off-by: @kisnwang

mindspore/nn/loss/loss.py

@@ -490,9 +490,9 @@ class MultiClassDiceLoss(_Loss):
 
     Inputs:
         - **y_pred** (Tensor) - Tensor of shape (N, C, ...). The y_pred dimension should be greater than 1. The data
-            type must be float16 or float32.
+          type must be float16 or float32.
         - **y** (Tensor) - Tensor of shape (N, C, ...). The y dimension should be greater than 1. The data type must be
-            float16 or float32.
+          loat16 or float32.
 
     Outputs:
         Tensor, a tensor of shape with the per-example sampled MultiClass Dice Losses.

mindspore/nn/metrics/roc.py

@@ -31,7 +31,7 @@ class ROC(Metric):
             range [0,num_classes-1]. Default: None.
 
     Examples:
-        >>> 1) binary classification example
+        >>> # 1) binary classification example
         >>> x = Tensor(np.array([3, 1, 4, 2]))
         >>> y = Tensor(np.array([0, 1, 2, 3]))
         >>> metric = ROC(pos_label=2)
@@ -42,7 +42,7 @@ class ROC(Metric):
         [0., 1, 1., 1., 1.]
         [5, 4, 3, 2, 1]
         >>>
-        >>> 2) multiclass classification example
+        >>> # 2) multiclass classification example
         >>> x = Tensor(np.array([[0.28, 0.55, 0.15, 0.05], [0.10, 0.20, 0.05, 0.05], [0.20, 0.05, 0.15, 0.05],
         ...                     [0.05, 0.05, 0.05, 0.75]]))
         >>> y = Tensor(np.array([0, 1, 2, 3]))
@@ -101,6 +101,7 @@ class ROC(Metric):
     def update(self, *inputs):
         """
         Update state with predictions and targets.
+
         Args:
             inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray.
                 In most cases (not strictly), y_pred is a list of floating numbers in range :math:`[0, 1]`