optimizer error info for not supported np.dtype and modify example of TopK

mindspore/common/tensor.py

@@ -69,8 +69,8 @@ class Tensor(Tensor_):
         valid_dtypes = (np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64,
                         np.float16, np.float32, np.float64, np.bool_)
         if isinstance(input_data, np.ndarray) and input_data.dtype not in valid_dtypes:
-            raise TypeError(f"For Tensor, the input_data is a numpy array whose data type is "
-                            f"{input_data.dtype} that is not supported to initialize a Tensor.")
+            raise TypeError(f"For Tensor, the input_data is a numpy array whose value is {input_data} and "
+                            f"data type is {input_data.dtype} that is not supported to initialize a Tensor.")
         if isinstance(input_data, (tuple, list)):
             if np.array(input_data).dtype not in valid_dtypes:
                 raise TypeError(f"For Tensor, the input_data is {input_data} that contain unsupported element.")

mindspore/ops/operations/nn_ops.py

@@ -1741,8 +1741,8 @@ class TopK(PrimitiveWithInfer):
         >>> input_x = Tensor([1, 2, 3, 4, 5], mindspore.float16)
         >>> k = 3
         >>> values, indices = topk(input_x, k)
-        >>> assert values == Tensor(np.array([5, 4, 3]), mstype.float16)
-        >>> assert indices == Tensor(np.array([4, 3, 2]), mstype.int32)
+        >>> assert values == Tensor(np.array([5, 4, 3]), mstype.float16).all()
+        >>> assert indices == Tensor(np.array([4, 3, 2]), mstype.int32).all()
     """
 
     @prim_attr_register