Added notation for graph-level seed access interfaces

mindspore/ops/composite/random_ops.py

@@ -22,6 +22,7 @@ from .multitype_ops import _constexpr_utils as const_utils
 from ...common import dtype as mstype
 from ...common.tensor import Tensor
 from ..._checkparam import Validator as validator
+from ..._checkparam import check_int_positive
 from ..._checkparam import Rel
 
 # set graph-level RNG seed
@@ -29,11 +30,36 @@ _GRAPH_SEED = 0
 
 @constexpr
 def set_seed(seed):
+    """
+    Set the graph-level seed.
+    Graph-level seed is used as a global variable, that can be used in different ops in case op-level seed is not set.
+    If op-level seed is 0, use graph-level seed; if op-level seed is also 0, the system would generate a
+    random seed.
+
+    Args:
+        seed(Int): the graph-level seed value that to be set.
+
+    Examples:
+        >>> C.set_seed(10)
+    """
+    check_int_positive(seed)
     global _GRAPH_SEED
     _GRAPH_SEED = seed
 
 @constexpr
 def get_seed():
+    """
+    Get the graph-level seed.
+    Graph-level seed is used as a global variable, that can be used in different ops in case op-level seed is not set.
+    If op-level seed is 0, use graph-level seed; if op-level seed is also 0, the system would generate a
+    random seed.
+
+    Returns:
+        Interger. The current graph-level seed.
+
+    Examples:
+        >>> C.get_seed(10)
+    """
     return _GRAPH_SEED
 
 
@@ -58,7 +84,6 @@ def normal(shape, mean, stddev, seed=0):
         >>> shape = (4, 16)
         >>> mean = Tensor(1.0, mstype.float32)
         >>> stddev = Tensor(1.0, mstype.float32)
-        >>> C.set_seed(10)
         >>> output = C.normal(shape, mean, stddev, seed=5)
     """
     mean_dtype = F.dtype(mean)