update the description of EmbeddingLookup operator and add the output of multinomial operator.

mindspore/nn/layer/embedding.py

@@ -173,7 +173,8 @@ class EmbeddingLookup(Cell):
     Inputs:
         - **input_indices** (Tensor) - The shape of tensor is :math:`(y_1, y_2, ..., y_S)`.
           Specifies the indices of elements of the original Tensor. Values can be out of range of embedding_table,
-          and the exceeding part will be filled with 0 in the output. Input_indices must only be a 2d tensor in
+          and the exceeding part will be filled with 0 in the output. Values does not support negative and the result
+          is undefined if values are negative. Input_indices must only be a 2d tensor in
           this interface when run in semi auto parallel/auto parallel mode.
 
     Outputs:

mindspore/ops/composite/random_ops.py

@@ -258,7 +258,7 @@ def multinomial(inputs, num_sample, replacement=True, seed=None):
         >>> input = Tensor([0, 9, 4, 0], mstype.float32)
         >>> output = C.multinomial(input, 2, True)
         >>> print(output)
-        [1 2]
+        [1 1]
     """
     shape = P.Shape()
     reshape = P.Reshape()

mindspore/ops/operations/array_ops.py

@@ -4580,7 +4580,8 @@ class EmbeddingLookup(PrimitiveWithInfer):
           This represents a Tensor slice, instead of the entire Tensor. Currently, the dimension is restricted to be 2.
         - **input_indices** (Tensor) - The shape of tensor is :math:`(y_1, y_2, ..., y_S)`.
           Specifies the indices of elements of the original Tensor. Values can be out of range of `input_params`,
-          and the exceeding part will be filled with 0 in the output.
+          and the exceeding part will be filled with 0 in the output. Values does not support negative and the result
+          is undefined if values are negative.
         - **offset** (int) - Specifies the offset value of this `input_params` slice. Thus the real indices
           are equal to `input_indices` minus `offset`.
 

mindspore/ops/operations/random_ops.py

@@ -498,6 +498,8 @@ class Multinomial(PrimitiveWithInfer):
         >>> input = Tensor([0., 9., 4., 0.], mstype.float32)
         >>> multinomial = ops.Multinomial(seed=10)
         >>> output = multinomial(input, 2)
+        >>> print(output)
+        [2 1]
     """
 
     @prim_attr_register