update doc string

python/tvm/tir/function.py

@@ -97,6 +97,41 @@ def specialize(self, param_map: Mapping[Var, Union[PrimExpr, Buffer]]):
         param_map : Mapping[Var, Union[PrimExpr, Buffer]]
             The mapping from function params to the instance
 
+        Examples
+        --------
+        We can define a Meta TIR function with symbolic shape:
+
+        .. code-block:: python
+
+            @tvm.script.tir
+            def mem_copy(a: ty.handle, b: ty.handle, m: ty.int32, n: ty.int32) -> None:
+                A = tir.match_buffer(a, (m, n), "float32")
+                B = tir.match_buffer(b, (m, n), "float32")
+
+                with tir.block([m, n], "") as [vi, vj]:
+                    B[vi, vj] = A[vi, vj]
+
+        Then we can make it specialized with given shapes or buffers.
+
+        .. code-block:: python
+
+            a, _, m, n = mem_copy.params
+            func = mem_copy.specialize({a: tir.decl_buffer((16, 16))})
+            # or
+            func = mem_copy.specialize({n: 16, m: 16})
+
+        The specialized function:
+
+        .. code-block:: python
+
+            @tvm.script.tir
+            def mem_copy_16_16(a: ty.handle, b: ty.handle) -> None:
+                A = tir.match_buffer(a, (16, 16), "float32")
+                B = tir.match_buffer(b, (16, 16), "float32")
+
+                with tir.block([16, 16], "") as [vi, vj]:
+                    B[vi, vj] = A[vi, vj]
+
         Returns
         -------
         func : PrimFunc