diff --git a/src/autodiff/gradfunc.jl b/src/autodiff/gradfunc.jl
index 94bbb91..0dc1b62 100644
--- a/src/autodiff/gradfunc.jl
+++ b/src/autodiff/gradfunc.jl
@@ -48,6 +48,41 @@ end
     (~protectf(g).f)(args...; kwargs...)
 end
 
+"""
+    gradient(Val(iloss), f, args::Tuple; kwargs...)
+    gradient(f, args; iloss::Int, kwargs...)
+    
+Calculate the gradient of `f(args...; kwargs...)` for reversible function `f` with regard to
+input `args`. The integer value `iloss` specifies the position of `loss` in `args`.
+
+!!! note
+    `iloss=1` is specially optimized, so putting the loss as the first parameter can avoid potential overhead.
+
+# Examples
+
+```jldoctest; setup=:(using NiLang)
+X = rand(2, 2)
+grads = NiLang.AD.gradient(Val(1), i_norm2, (0.0, X))
+grads[2] ≈ 2 .* X
+
+# output
+true
+```
+
+Note that `gradient` calculation is disabled for container with integers:
+
+```jldoctest; setup=:(using NiLang)
+X = rand(Int, 2, 2)
+NiLang.AD.gradient(Val(1), i_sum, (0, X))[2]
+
+# output
+2×2 Matrix{Int64}:
+ 0  0
+ 0  0
+```
+"""
+gradient
+
 @generated function gradient(::Val{iloss}, f, args::NTuple{N,Any}; kwargs...) where {iloss,N}
     newres = gensym()
     newargs = Any[:(GVar($newres[$i])) for i=1:N]