fix Randperm expample and infer check

mindspore/ops/operations/inner_ops.py

@@ -77,8 +77,7 @@ class Randperm(PrimitiveWithInfer):
         dtype (mindspore.dtype): The type of output. Default: mindspore.int32.
 
     Inputs:
-        - **n** (Tensor[int]) - The input tensor with shape: (1,) and the number must be in (0, `max_length`].
-          Default: 1.
+        - **n** (Tensor[int32]) - The input tensor with shape: (1,) and the number must be in [0, `max_length`].
 
     Outputs:
         - **output** (Tensor) - The output Tensor with shape: (`max_length`,) and type: `dtype`.
@@ -87,6 +86,7 @@ class Randperm(PrimitiveWithInfer):
         TypeError: If neither `max_length` nor `pad` is an int.
         TypeError: If `n` is not a Tensor.
         TypeError: If `n` has non-Int elements.
+        TypeError: If `n` has negative elements.
 
     Supported Platforms:
         ``Ascend``
@@ -96,7 +96,7 @@ class Randperm(PrimitiveWithInfer):
         >>> n = Tensor([20], dtype=mindspore.int32)
         >>> output = randperm(n)
         >>> print(output)
-        [15 6 11 19 14 16 9 5 13 18 4 10 8 0 17 2 14 1 12 3 7
+        [15 6 11 19 14 16 9 5 13 18 4 10 8 0 17 2 1 12 3 7
          -1 -1 -1 -1 -1 -1 -1 -1 -1 -1]
     """
 
@@ -105,7 +105,7 @@ class Randperm(PrimitiveWithInfer):
         """Initialize Randperm"""
         validator.check_value_type("pad", pad, [int], self.name)
         validator.check_value_type("max_length", max_length, [int], self.name)
-        validator.check_int(max_length, 1, Rel.GE, "1", self.name)
+        validator.check_int(max_length, 1, Rel.GE, "max_length", self.name)
         self.dtype = dtype
         self.max_length = max_length
         self.init_prim_io_names(inputs=[], outputs=['output'])