Rectification of operator ease of use part 3

mindspore/nn/loss/loss.py

@@ -1182,7 +1182,7 @@ class FocalLoss(Loss):
     The loss function proposed by Kaiming team in their paper ``Focal Loss for Dense Object Detection`` improves the
     effect of image object detection. It is a loss function to solve the imbalance of categories and the difference of
     classification difficulty. If you want to learn more, please refer to the paper.
-    `Focal Loss for Dense Object Detection https://arxiv.org/pdf/1708.02002.pdf`_. The function is shown as follows:
+    `Focal Loss for Dense Object Detection <https://arxiv.org/pdf/1708.02002.pdf>`_. The function is shown as follows:
 
     .. math::
         FL(p_t) = -(1-p_t)^\gamma log(p_t)

mindspore/ops/operations/comm_ops.py

@@ -171,9 +171,9 @@ class AllGather(PrimitiveWithInfer):
         ...     def construct(self, x):
         ...         return self.allgather(x)
         ...
-        >>> input_ = Tensor(np.ones([2, 8]).astype(np.float32))
+        >>> input_x = Tensor(np.ones([2, 8]).astype(np.float32))
         >>> net = Net()
-        >>> output = net(input_)
+        >>> output = net(input_x)
         >>> print(output)
         [[1. 1. 1. 1. 1. 1. 1. 1.]
          [1. 1. 1. 1. 1. 1. 1. 1.]
@@ -462,9 +462,9 @@ class Broadcast(PrimitiveWithInfer):
         ...     def construct(self, x):
         ...         return self.broadcast((x,))
         ...
-        >>> input_ = Tensor(np.ones([2, 4]).astype(np.int32))
+        >>> input_x = Tensor(np.ones([2, 4]).astype(np.int32))
         >>> net = Net()
-        >>> output = net(input_)
+        >>> output = net(input_x)
         >>> print(output)
         (Tensor(shape[2,4], dtype=Int32, value=
         [[1, 1, 1, 1],

mindspore/ops/operations/other_ops.py

@@ -34,10 +34,11 @@ class Assign(Primitive):
 
     Inputs:
         - **variable** (Parameter) - The `Parameter`.
-        - **value** (Tensor) - The value to be assigned.
+          :math:`(N,*)` where :math:`*` means ,any number of additional dimensions, its rank should less than 8.
+        - **value** (Tensor) - The value to be assigned, has the same shape with `variable`.
 
     Outputs:
-        Tensor, has the same type as original `variable`.
+        Tensor, has the same data type and shape as original `variable`.
 
     Raises:
         TypeError: If `variable` is not a Parameter.