!10022 fix example and Validator for LogUniformCandidateSampler

From: @yanzhenxiang2020
Reviewed-by: 
Signed-off-by:

mindspore/ops/operations/array_ops.py

@@ -4134,7 +4134,7 @@ class Meshgrid(PrimitiveWithInfer):
     """
     Generates coordinate matrices from given coordinate tensors.
 
-    Given N one-dimensional coordinate tensors, returns a list outputs of N N-D
+    Given N one-dimensional coordinate tensors, returns a tuple outputs of N N-D
     coordinate tensors for evaluating expressions on an N-D grid.
 
 
@@ -4144,12 +4144,15 @@ class Meshgrid(PrimitiveWithInfer):
           instructions for the first two dimensions are swapped.
 
     Inputs:
-        - **input_x** (Union[tuple, list]) - A Tuple or list of N 1-D Tensor objects.
-          The length of input_x should be greater than 1
+        - **input** (Union[tuple]) - A Tuple of N 1-D Tensor objects.
+          The length of input should be greater than 1
 
     Outputs:
         Tensors, A Tuple of N N-D Tensor objects.
 
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> x = Tensor(np.array([1, 2, 3, 4]).astype(np.int32))
         >>> y = Tensor(np.array([5, 6, 7]).astype(np.int32))
@@ -4158,7 +4161,7 @@ class Meshgrid(PrimitiveWithInfer):
         >>> meshgrid = ops.Meshgrid(indexing="xy")
         >>> output = meshgrid(inputs)
         >>> print(output)
-        (Tensor(shape=[3, 4, 6], dtype=Int32, value=
+        (Tensor(shape=[3, 4, 5], dtype=Int32, value=
          [[[1, 1, 1, 1, 1],
            [2, 2, 2, 2, 2],
            [3, 3, 3, 3, 3],
@@ -4171,7 +4174,7 @@ class Meshgrid(PrimitiveWithInfer):
            [2, 2, 2, 2, 2],
            [3, 3, 3, 3, 3],
            [4, 4, 4, 4, 4]]]),
-         Tensor(shape=[3, 4, 6], dtype=Int32, value=
+         Tensor(shape=[3, 4, 5], dtype=Int32, value=
          [[[5, 5, 5, 5, 5],
            [5, 5, 5, 5, 5],
            [5, 5, 5, 5, 5],
@@ -4184,7 +4187,7 @@ class Meshgrid(PrimitiveWithInfer):
            [7, 7, 7, 7, 7],
            [7, 7, 7, 7, 7],
            [7, 7, 7, 7, 7]]]),
-         Tensor(shape=[3, 4, 6], dtype=Int32, value=
+         Tensor(shape=[3, 4, 5], dtype=Int32, value=
          [[[8, 9, 0, 1, 2],
            [8, 9, 0, 1, 2],
            [8, 9, 0, 1, 2],
@@ -4208,12 +4211,12 @@ class Meshgrid(PrimitiveWithInfer):
         self.indexing = indexing
 
     def infer_shape(self, x_shape):
-        validator.check_value_type("shape", x_shape, [tuple, list], self.name)
-        validator.check_int(len(x_shape), 2, Rel.GE, "len of input_x", self.name)
+        validator.check_value_type("shape", x_shape, [tuple], self.name)
+        validator.check_int(len(x_shape), 2, Rel.GE, "len of input", self.name)
         n = len(x_shape)
         shape_0 = []
         for s in x_shape:
-            validator.check_int(len(s), 1, Rel.EQ, 'each_input_rank', self.name)
+            validator.check_int(len(s), 1, Rel.EQ, 'each input rank', self.name)
             shape_0.append(s[0])
         if self.indexing == "xy":
             shape_0[0], shape_0[1] = shape_0[1], shape_0[0]
@@ -4221,7 +4224,7 @@ class Meshgrid(PrimitiveWithInfer):
         return out_shape
 
     def infer_dtype(self, x_type):
-        validator.check_subclass("input_x[0]", x_type[0], mstype.tensor, self.name)
+        validator.check_subclass("input[0]", x_type[0], mstype.tensor, self.name)
         n = len(x_type)
         for i in range(1, n):
             validator.check('x_type[%d]' % i, x_type[i], 'base', x_type[0], Rel.EQ, self.name, TypeError)

mindspore/ops/operations/random_ops.py

@@ -621,14 +621,14 @@ class LogUniformCandidateSampler(PrimitiveWithInfer):
         ``Ascend``
 
     Examples:
-        >>> sampler = ops.LogUniformCandidateSampler(1, 5, True, 5)
+        >>> sampler = ops.LogUniformCandidateSampler(2, 5, True, 5)
         >>> output1, output2, output3 = sampler(Tensor(np.array([[1, 7], [0, 4], [3, 3]])))
         >>> print(output1, output2, output3)
-        [3, 2, 0, 4, 1],
-        [[9.23129916e-01, 4.93363708e-01],
-         [9.92489874e-01, 6.58063710e-01],
-         [7.35534430e-01, 7.35534430e-01]],
-        [7.35534430e-01, 8.26258004e-01, 9.92489874e-01, 6.58063710e-01, 9.23129916e-01]
+        [3 2 0 4 1]
+        [[0.92312991 0.49336370]
+         [0.99248987 0.65806371]
+         [0.73553443 0.73553443]]
+        [0.73553443 0.82625800 0.99248987 0.65806371 0.92312991]
 
     """
 
@@ -645,14 +645,14 @@ class LogUniformCandidateSampler(PrimitiveWithInfer):
         self.num_true = Validator.check_number("num_true", num_true, 1, Rel.GE, self.name)
         self.num_sampled = Validator.check_number("num_sampled", num_sampled, 1, Rel.GE, self.name)
         if unique:
-            Validator.check_number("range_max", range_max, num_sampled, Rel.GE, self.name)
+            Validator.check("range_max", range_max, "num_sampled", num_sampled, Rel.GE, self.name)
         self.range_max = range_max
         self.unique = unique
-        self.seed = seed
+        self.seed = Validator.check_number("seed", seed, 0, Rel.GE, self.name)
 
     def infer_shape(self, true_classes_shape):
-        Validator.check("true_classes shape rank", len(true_classes_shape), "expect", 2, Rel.EQ, self.name)
-        Validator.check_int(true_classes_shape[1], self.num_true, Rel.EQ, 'true_classes_shape', self.name)
+        Validator.check_int(len(true_classes_shape), 2, Rel.EQ, "dim of true_classes", self.name)
+        Validator.check("true_classes_shape[1]", true_classes_shape[1], "num_true", self.num_true, Rel.EQ, self.name)
         return (self.num_sampled,), true_classes_shape, (self.num_sampled,)
 
     def infer_dtype(self, true_classes_type):