fix LRN

mindspore/ops/operations/array_ops.py

@@ -988,7 +988,7 @@ class InvertPermutation(PrimitiveWithInfer):
         values can not be negative.
 
     Inputs:
-        - **input_x** (Union(tuple[int]) - The input tuple is constructed by multiple
+        - **input_x** (Union(tuple[int], list[int]) - The input is constructed by multiple
           integers, i.e., :math:`(y_1, y_2, ..., y_S)` representing the indices.
           The values must include 0. There can be no duplicate values or negative values.
           Only constant value is allowed.
@@ -1016,6 +1016,8 @@ class InvertPermutation(PrimitiveWithInfer):
         validator.check_value_type("shape", x_shp, [tuple, list], self.name)
         if mstype.issubclass_(x['dtype'], mstype.tensor):
             raise ValueError(f'For \'{self.name}\' the input value must be non-Tensor.')
+        for i, value in enumerate(x_value):
+            validator.check_value_type("input[%d]" % i, value, [int], self.name)
         z = [x_value[i] for i in range(len(x_value))]
         z.sort()
 

mindspore/ops/operations/nn_ops.py

@@ -4974,6 +4974,7 @@ class LRN(PrimitiveWithInfer):
         validator.check_value_type("beta", beta, [float], self.name)
         validator.check_value_type("norm_region", norm_region, [str], self.name)
         validator.check_string('norm_region', norm_region, ['ACROSS_CHANNELS'], self.name)
+        validator.check_integer("depth_radius", depth_radius, 0, Rel.GE, self.name)
 
     def infer_dtype(self, x_dtype):
         validator.check_tensor_type_same({"x": x_dtype}, (mstype.float16, mstype.float32,), self.name)