fix docs

docs/api/api_python/ops/mindspore.ops.func_broadcast_to.rst

@@ -11,17 +11,17 @@ mindspore.ops.broadcast_to
 
     - 如果不相等，分以下三种情况：
 
-        - 情况一：如果目标shape该维的值为-1， 则输出shape该维的值为对应输入shape该维的值。比如说输入shape为 :math:`(3, 3)` ，目标shape为 :math:`(-1, 3)` ，则输出shape为 :math:`(3, 3)` ；
+      - 情况一：如果目标shape该维的值为-1，则输出shape该维的值为对应输入shape该维的值。比如说输入shape为 :math:`(3, 3)` ，目标shape为 :math:`(-1, 3)` ，则输出shape为 :math:`(3, 3)` ；
 
-        - 情况二：如果目标shape该维的值不为-1，但是输入shape该维的值为1，则输出shape该维的值为目标shape该维的值。比如说输入shape为 :math:` (1, 3)` ，目标shape为 :math:`(8, 3)` ，则输出shape为 :math:`(8, 3)` ；
+      - 情况二：如果目标shape该维的值不为-1，但是输入shape该维的值为1，则输出shape该维的值为目标shape该维的值。比如说输入shape为 :math:`(1, 3)` ，目标shape为 :math:`(8, 3)` ，则输出shape为 :math:`(8, 3)` ；
 
-        - 情况三：如果两个shape对应值不满足以上情况则说明不支持由输入shape广播到目标shape。
+      - 情况三：如果两个shape对应值不满足以上情况则说明不支持由输入shape广播到目标shape。
 
     至此输出shape后面m维就确定好了，现在看一下前面 :math:`*` 维，有以下两种情况：
 
-    - 如果额外的 :math:`*` 维中不含有-1，则输入shape从低维度补充维度使之与目标shape维度一致，比如说目标shape为 :math:` (3, 1, 4, 1, 5, 9)` ，输入shape为 :math:`(1, 5, 9)` ，则输入shape增维变成 :math:`(1, 1, 1, 1, 5, 9)`，根据上面提到的情况二可以得出输出shape为 :math:` (3, 1, 4, 1, 5, 9)`；
+    - 如果额外的 :math:`*` 维中不含有-1，则输入shape从低维度补充维度使之与目标shape维度一致，比如说目标shape为 :math:`(3, 1, 4, 1, 5, 9)` ，输入shape为 :math:`(1, 5, 9)` ，则输入shape增维变成 :math:`(1, 1, 1, 1, 5, 9)`，根据上面提到的情况二可以得出输出shape为 :math:`(3, 1, 4, 1, 5, 9)`；
 
-    - 如果额外的 :math:`*` 维中含有-1，说明此时该-1对应一个不存在的维度，不支持广播。比如说目标shape为 :math:` (3, -1, 4, 1, 5, 9)` ，输入shape为 :math:`(1, 5, 9)` ，此时不进行增维处理，而是直接报错。
+    - 如果额外的 :math:`*` 维中含有-1，说明此时该-1对应一个不存在的维度，不支持广播。比如说目标shape为 :math:`(3, -1, 4, 1, 5, 9)` ，输入shape为 :math:`(1, 5, 9)` ，此时不进行增维处理，而是直接报错。
 
     参数：
         - **x** (Tensor) - 第一个输入，任意维度的Tensor，数据类型为float16、float32、int32、int8、uint8、bool。

mindspore/python/mindspore/ops/function/array_func.py

@@ -3251,37 +3251,40 @@ def affine_grid(theta, output_size, align_corners=False):
 def broadcast_to(x, shape):
     """
     Broadcasts input tensor to a given shape. The dim of input shape must be smaller
-    than or equal to that of target shape, suppose input shape :math:`(x1, x2, ..., xm)`,
-    target shape :math:`(*, y_1, y_2, ..., y_m)`. The broadcast rules are as follows:
+    than or equal to that of target shape. Suppose input shape is :math:`(x1, x2, ..., xm)`,
+    target shape is :math:`(*, y_1, y_2, ..., y_m)`, where :math:`*` means any additional dimension.
+    The broadcast rules are as follows:
 
     Compare the value of `x_m` and `y_m`, `x_{m-1}` and `y_{m-1}`, ..., `x_1` and `y_1` consecutively and
     decide whether these shapes are broadcastable and what the broadcast result is.
 
     If the value pairs at a specific dim are equal, then that value goes right into that dim of output shape.
-    With an input shape :math:`(2, 3)`, target shape :math:`(2, 3)` , the inferred outpyt shape is :math:`(2, 3)`.
+    With an input shape :math:`(2, 3)`, target shape :math:`(2, 3)` , the inferred output shape is :math:`(2, 3)`.
 
     If the value pairs are unequal, there are three cases:
 
-    Case 1: Value of target shape is -1, then the value of the output shape is that of the input shape's.
-    With an input shape :math:`(3, 3)`, target shape :math:`(-1, 3)`, the output shape is :math:`(3, 3)`.
+    Case 1: If the value of the target shape in the dimension is -1, the value of the
+    output shape in the dimension is the value of the corresponding input shape in the dimension.
 
-    Case 2: Value of target shape is not -1 but the value ot the input shape is 1, then the value of the output shape
-    is that of the target shape's. With an input shape :math:`(1, 3)`, target
+    Case 2: If the value of target shape in the dimension is not -1, but the corresponding
+    value in the input shape is 1, then the corresponding value of the output shape
+    is that of the target shape. With an input shape :math:`(1, 3)`, target
     shape :math:`(8, 3)`, the output shape is :math:`(8, 3)`.
 
-    Case 3: All other cases mean that the two shapes are not broadcastable.
+    Case 3: If the corresponding values of the two shapes do not satisfy the above cases,
+    it means that broadcasting from the input shape to the target shape is not supported.
 
     So far we got the last m dims of the outshape, now focus on the first :math:`*` dims, there are
     two cases:
 
     If the first :math:`*` dims of output shape does not have -1 in it, then fill the input
     shape with ones until their length are the same, and then refer to
-    Case 2 mentioned above to calculate the output shape. With target shape :math:` (3, 1, 4, 1, 5, 9)`,
+    Case 2 mentioned above to calculate the output shape. With target shape :math:`(3, 1, 4, 1, 5, 9)`,
     input shape :math:`(1, 5, 9)`, the filled input shape will be :math:`(1, 1, 1, 1, 5, 9)` and thus the
-    output shape is :math:` (3, 1, 4, 1, 5, 9)`.
+    output shape is :math:`(3, 1, 4, 1, 5, 9)`.
 
     If the first :math:`*` dims of output shape have -1 in it, it implies this -1 is conrresponding to
-    a non-existing dim so they're not broadcastable. With target shape :math:` (3, -1, 4, 1, 5, 9)`,
+    a non-existing dim so they're not broadcastable. With target shape :math:`(3, -1, 4, 1, 5, 9)`,
     input shape :math:`(1, 5, 9)`, instead of operating the dim-filling process first, it raises errors directly.
 
     Args:

mindspore/python/mindspore/ops/operations/random_ops.py

@@ -807,7 +807,7 @@ class UniformCandidateSampler(PrimitiveWithInfer):
 
     Examples:
         >>> sampler = ops.UniformCandidateSampler(1, 3, False, 4, 1)
-        >>> output1, output2, output3 = sampler(Tensor(np.array([[1], [3], [4], [6], [3]], dtype=np.int32)))
+        >>> output1, output2, output3 = sampler(Tensor(np.array([[1], [3], [4], [6], [3]], dtype=np.int64)))
         >>> print(output1.shape)
         (3,)
         >>> print(output2.shape)