fix gather_nd tensor rst

docs/api/api_python/mindspore/mindspore.Tensor.rst

@@ -385,30 +385,31 @@ mindspore.Tensor
 
         与输入的张量具有相同的数据类型的Tensor。
 
-    .. py:method:: gather_nd(input_x, indices)
+    .. py:method:: gather_nd(indices)
 
-        按索引从张量中获取切片。
-        使用给定的索引从具有指定形状的张量中搜集切片。
-        `indices` 是一个K维的整数张量，假定它的K-1维张量中的每一个元素是 `input_x` 的切片，那么有：
+        按索引从输入Tensor中获取切片。
+        使用给定的索引从具有指定形状的输入Tensor中搜集切片。
+        输入Tensor的shape是 :math:`(N,*)` ，其中 :math:`*` 表示任意数量的附加维度。为了表达方便，
+        将其定义为`input_x`。
+        `indices` 是一个K维的整数张量，假定它的K-1维张量中的每一个元素是输入Tensor的切片，那么有：
 
         .. math::
             output[(i_0, ..., i_{K-2})] = input\_x[indices[(i_0, ..., i_{K-2})]]
         
-        `indices` 的最后一维不能超过 `input_x` 的秩：
+        `indices` 的最后一维不能超过输入Tensor的秩：
         :math:`indices.shape[-1] <= input\_x.rank`。
 
         **参数：**
 
-        - **input_x** (Tensor) - 待搜集元素的目标张量，它的shape是 :math:`(N,*)` ，其中 :math:`*` 表示任意数量的附加维度。
         - **indices** (Tensor) - 获取收集元素的索引张量，其数据类型包括：int32，int64。
 
         **返回：**
 
-        Tensor，具有与入参 `input_x` 相同的数据类型，shape维度为indices_shape[:-1] + input_x_shape[indices_shape[-1]:]。
+        Tensor，具有与输入Tensor相同的数据类型，shape维度为 :math:`indices\_shape[:-1] + input\_x\_shape[indices\_shape[-1]:]`。
 
         **异常：**
 
-        - **ValueError** - 如果 `input_x` 的shape长度小于 `indices` 的最后一个维度。
+        - **ValueError** - 如果输入Tensor的shape长度小于 `indices` 的最后一个维度。
 
     .. py:method:: ger(x)
 

mindspore/python/mindspore/common/tensor.py

@@ -2652,27 +2652,28 @@ class Tensor(Tensor_):
 
     def gather_nd(self, indices):
         r"""
-        Gathers slices from a tensor by indices.
-        Using given indices to gather slices from a tensor with a specified shape.
-        `input_x` is a target tensor of the element to be collected,
-        The shape is :math:`(N,*)` where :math:`*` means any number of additional dimensions.
+        Gathers slices from a input tensor by indices.
+        Using given indices to gather slices from a input tensor with a specified shape.
+        input tensor's shape is :math:`(N,*)` where :math:`*` means any number of additional dimensions. For convenience
+        define it as `input_x`.
         `indices` is an K-dimensional integer tensor. Suppose that it is a (K-1)-dimensional tensor and each element
-        of it defines a slice of `input_x`:
+        of it defines a slice of input tensor:
 
         .. math::
             output[(i_0, ..., i_{K-2})] = input\_x[indices[(i_0, ..., i_{K-2})]]
 
-        The last dimension of `indices` can not more than the rank of `input_x`:
+        The last dimension of `indices` can not more than the rank of input tensor:
         :math:`indices.shape[-1] <= input\_x.rank`.
 
         Args:
             indices (Tensor): The index tensor that gets the collected elements, with int32 or int64 data type.
 
         Returns:
-            Tensor, has the same type as `input_x` and the shape is indices_shape[:-1] + x_shape[indices_shape[-1]:].
+            Tensor, has the same type as input tensor and the shape is:
+            :math:`indices\_shape[:-1] + input\_x\_shape[indices\_shape[-1]:]`.
 
         Raises:
-            ValueError: If length of shape of `input_x` is less than the last dimension of `indices`.
+            ValueError: If length of shape of input tensor is less than the last dimension of `indices`.
 
         Supported Platforms:
             ``Ascend`` ``GPU`` ``CPU``