fix file clean

mindspore/ops/_grad/grad_array_ops.py

@@ -1060,6 +1060,7 @@ def get_bprop_unique(self):
 
     return bprop
 
+
 @bprop_getters.register(P.MaskedSelect)
 def get_bprop_masked_select(self):
     """Generate bprop for MaskedSelect"""

mindspore/ops/operations/array_ops.py

@@ -5886,16 +5886,18 @@ class Range(PrimitiveWithCheck):
             return Tensor(np.arange(start, limit, delat), dtype=start_value.dtype)
         return None
 
+
 class MaskedSelect(PrimitiveWithCheck):
     """
     Returns a new 1-D Tensor which indexes the input tensor according to the boolean mask.
+    The shapes of the mask tensor and the input tensor don’t need to match, but they must be broadcastable.
 
     Inputs:
         - **x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
-        - **mask** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
+        - **mask** (Tensor[bool]) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
 
     Outputs:
-        Tensor, the shape of tensor is the same as `input_x`, :math:`(x_1, x_2, ..., x_R)`.
+        A 1-D Tensor, with the same type as x.
 
     Raises:
         TypeError: If `x` is not a Tensor.
@@ -5905,7 +5907,7 @@ class MaskedSelect(PrimitiveWithCheck):
 
     Examples:
         >>> x = Tensor(np.array([1, 2, 3, 4]), mindspore.int64)
-        >>> mask = Tensor(np.array([1, 0, 1, 0]), mindspore.bool)
+        >>> mask = Tensor(np.array([1, 0, 1, 0]), mindspore.bool_)
         >>> output = ops.MaskedSelect()(x, mask)
         >>> print(output)
         [1 3]
@@ -5921,6 +5923,7 @@ class MaskedSelect(PrimitiveWithCheck):
     def check_dtype(self, x_dtype, mask_dtype):
         validator.check_tensor_dtype_valid('mask', mask_dtype, [mstype.bool_], self.name)
 
+
 class SearchSorted(PrimitiveWithInfer):
     """
     Find the indices from the innermost dimension of `sequence` such that the order of the innermost dimension