diff --git a/imperative/python/megengine/core/ops/custom/__init__.py b/imperative/python/megengine/core/ops/custom/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f701da0e69b331626178e4bc2f1d1574cb0792a
--- /dev/null
+++ b/imperative/python/megengine/core/ops/custom/__init__.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
+from ..._imperative_rt.ops import _custom
+
+__all__ = []
+
+for k, v in _custom.__dict__.items():
+    globals()[k] = v
+    __all__.append(k)
+
+
+def gen_custom_op_maker(custom_op_name):
+    def op_maker(**kwargs):
+        return make_custom_op(custom_op_name, kwargs)
+    return op_maker
+
+
+def load(lib_path):
+    op_in_this_lib = install(lib_path[0:-3], lib_path)
+    for op in op_in_this_lib:
+        op_maker = gen_custom_op_maker(op)
+        globals()[op] = op_maker
+        __all__.append(op)
diff --git a/imperative/python/megengine/tools/load_network_and_run.py b/imperative/python/megengine/tools/load_network_and_run.py
index 46a288476a594f3ef8c875dfafdac6ec7f1857ed..aebe19073323b32b63c3300a32c25bcc3d843f64 100755
--- a/imperative/python/megengine/tools/load_network_and_run.py
+++ b/imperative/python/megengine/tools/load_network_and_run.py
@@ -13,6 +13,7 @@ from collections import OrderedDict
 import numpy as np
 
 import megengine as mge
+from megengine.core.ops import custom
 from megengine.core.tensor import megbrain_graph as G
 from megengine.device import get_device_count, set_default_device
 from megengine.functional.debug_param import set_execution_strategy
@@ -397,6 +398,10 @@ def main():
         type=str,
         help="Record the static graph's static memory info.",
     )
+    parser.add_argument(
+        "--custom-op-lib", type=str, help="path of the custom op",
+    )
+
     args = parser.parse_args()
 
     if args.verbose:
@@ -409,6 +414,8 @@ def main():
 
     if args.dump_cpp_model:
         args.embed_input = True
+    if args.custom_op_lib is not None:
+        custom.load(args.custom_op_lib)
 
     logger.info("loading model ...")
     ret = G.load_graph(args.net)
diff --git a/imperative/python/src/ops.cpp b/imperative/python/src/ops.cpp
index 5cbefadc5c6123e00a421fe4ef76181a30b24c20..2ecfadf585ea93dd1644287701e2fade35e37d8c 100644
--- a/imperative/python/src/ops.cpp
+++ b/imperative/python/src/ops.cpp
@@ -607,4 +607,107 @@ void init_ops(py::module m) {
         .def("compile", [](PySubgraphBuilder& self, int gopt_level){
             return (std::shared_ptr<OpDef>)CompiledOp::make(self.build(), gopt_level);
         });
+    
+    auto custom = submodule(m, "_custom");
+    init_custom(custom);
+}
+
+#define CUSTOM_CASE_TO_PARSE_NON_LIST(dyn_type, static_type)                \
+    case mgb::custom::ParamDynType::dyn_type: {                             \
+        param_val = py::handle(kv.second).cast<static_type>();              \
+        break;                                                              \
+    }
+
+#define CUSTOM_CASE_TO_PARSE_LIST(dyn_type, static_type)                    \
+    case mgb::custom::ParamDynType::dyn_type: {                             \
+        auto pyvals = py::handle(kv.second).cast<py::list>();               \
+        static_type vals;                                                   \
+        using basic_type =                                                  \
+            mgb::custom::get_vector_template_arg_type<static_type>::type;   \
+        for (auto &pyval: pyvals) {                                         \
+            vals.push_back(py::handle(pyval).cast<basic_type>());           \
+        }                                                                   \
+        param_val = vals;                                                   \
+        break;                                                              \
+    }
+
+PyObject *make_custom_op(PyObject *self, PyObject **args, Py_ssize_t nargs, PyObject *kwnames) {
+    auto op_name = py::handle(args[0]).cast<std::string>();
+    auto kwargs = py::handle(args[1]).cast<py::dict>();
+
+    std::shared_ptr<OpDef> opdef = CustomOpDefFactory::inst()->create_opdef(op_name);
+    auto &custom_opdef = static_cast<mgb::imperative::CustomOpDef&>(*opdef);
+    auto &param = custom_opdef.param();
+
+    for (auto &&kv: kwargs) {
+        std::string param_name = py::handle(kv.first).cast<std::string>();
+        std::string type_name = py::handle(kv.second).ptr()->ob_type->tp_name;
+        
+        if (!param.exist(param_name)) {
+            mgb_log_warn(
+                "op %s have no param named %s, ignore this param parsed from python",
+                op_name.c_str(), param_name.c_str()
+            );
+            continue;
+        }
+
+        auto& param_val = param[param_name];
+        switch (param_val.type()) {
+            CUSTOM_FOR_EACH_BASIC_PARAMTYPE(CUSTOM_CASE_TO_PARSE_NON_LIST)
+            CUSTOM_FOR_STRING_PARAMTYPE(CUSTOM_CASE_TO_PARSE_NON_LIST)
+            CUSTOM_FOR_EACH_BASIC_LIST_PARAMTYPE(CUSTOM_CASE_TO_PARSE_LIST)
+            CUSTOM_FOR_BOOL_LIST_PARAMTYPE(CUSTOM_CASE_TO_PARSE_LIST)
+            CUSTOM_FOR_STRING_LIST_PARAMTYPE(CUSTOM_CASE_TO_PARSE_LIST)
+            default: {
+                mgb_assert(
+                    false, "param dtype of %s:%s is invalid",
+                    op_name.c_str(), param_name.c_str()
+                );
+            }
+        }
+    }
+
+    PyTypeObject* pytype;
+    pytype = &PyOpType(OpDef);
+    PyObject* obj = pytype->tp_alloc(pytype, 0);
+    reinterpret_cast<PyOp(OpDef)*>(obj)->op = opdef;
+    
+    return obj;
+}
+
+#undef CUSTOM_CASE_TO_PARSE_LIST
+#undef CUSTOM_CASE_TO_PARSE_NON_LIST
+
+py::list install_custom(const std::string &name, const std::string &path) {
+    py::list ret;
+    const auto &ops_in_lib = mgb::custom::LibManager::inst()->install(name, path);
+    for (const auto &op: ops_in_lib) {
+        ret.append(op);
+    }
+    return std::move(ret);
+}
+
+bool uninstall_custom(const std::string &name) {
+    return mgb::custom::LibManager::inst()->uninstall(name);
+}
+
+py::list get_custom_op_list(void) {
+    std::vector<std::string> all_ops = CustomOpDefFactory::inst()->op_list();
+    py::list ret;
+    for (auto &op: all_ops) {
+        ret.append(op);
+    }
+    return std::move(ret);
+}
+
+void init_custom(pybind11::module m) {
+    m.def("install", &install_custom);
+    m.def("uninstall", &uninstall_custom);
+    m.def("get_custom_op_list", &get_custom_op_list);
+
+    static PyMethodDef method_def = {
+        "make_custom_op", (PyCFunction)make_custom_op, METH_FASTCALL, ""
+    };
+    auto* func = PyCFunction_NewEx(&method_def, nullptr, nullptr);
+    pybind11::setattr(m, method_def.ml_name, func);
 }
diff --git a/imperative/python/src/ops.h b/imperative/python/src/ops.h
index f230c8b5526c70e22610c6a46f6aacd9e79365a8..6f32528872e108a16561dcaca4d88678f264e34e 100644
--- a/imperative/python/src/ops.h
+++ b/imperative/python/src/ops.h
@@ -16,6 +16,7 @@
 
 #include "megdnn/opr_param_defs.h"
 #include "megbrain/opr/param_defs.h"
+#include "megbrain/imperative/ops/custom_opdef.h"
 
 namespace PYBIND11_NAMESPACE {
 namespace detail {
@@ -35,3 +36,4 @@ FOR_EACH_BIT_COMBINED_ENUM_PARAM(ENUM_CASTER_DEF)
 } // PYBIND11_NAMESPACE
 
 void init_ops(pybind11::module m);
+void init_custom(pybind11::module m);
diff --git a/imperative/src/impl/ops/custom_opdef.cpp b/imperative/src/impl/ops/custom_opdef.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..d3376f6e2cdf3b4b6dddc96e98452c4e79d57a0b
--- /dev/null
+++ b/imperative/src/impl/ops/custom_opdef.cpp
@@ -0,0 +1,304 @@
+/**
+ * \file imperative/src/impl/ops/custom_opdef.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ */
+
+#include "megbrain/imperative/ops/custom_opdef.h"
+#include "megbrain/opr/custom_opnode.h"
+#include "megbrain/custom/data_adaptor.h"
+#include "../op_trait.h"
+
+namespace mgb {
+namespace imperative {
+
+MGB_DYN_TYPE_OBJ_FINAL_IMPL(CustomOpDef);
+
+CustomOpDef::CustomOpDef(const std::shared_ptr<const custom::CustomOp> &op)
+    : m_op(op), m_param(op->param_info()) {}
+
+CustomOpDef::CustomOpDef(const std::shared_ptr<const custom::CustomOp> &op,
+                         const custom::Param &param)
+    : m_op(op), m_param(param) {}
+
+void CustomOpDef::param(const custom::Param &rhs) {
+    m_param = rhs;
+}
+
+custom::Param &CustomOpDef::param(void) {
+    return m_param;
+}
+
+custom::Param CustomOpDef::param(void) const {
+    return m_param;
+}
+
+size_t CustomOpDef::input_num(void) const {
+    return m_op->input_num();
+}
+
+size_t CustomOpDef::output_num(void) const {
+    return m_op->output_num();
+}
+
+std::string CustomOpDef::name(void) const {
+    return m_op->op_type();
+}
+
+custom::RunTimeId CustomOpDef::runtime_id(void) const {
+    return m_op->runtime_id();
+}
+
+const std::shared_ptr<const custom::CustomOp> &CustomOpDef::impl(void) const {
+    return m_op;
+}
+
+void CustomOpDef::compute(const SmallVector<DeviceTensorND> &inputs,
+                          SmallVector<DeviceTensorND> *outputs) const {
+    std::vector<custom::Tensor> custom_inputs = 
+        custom::to_custom<DeviceTensorND, custom::Tensor>(inputs);
+    std::vector<custom::Tensor> custom_outputs =
+        custom::to_custom<DeviceTensorND, custom::Tensor>(*outputs);
+    m_op->compute(custom_inputs, this->m_param, custom_outputs);
+}
+
+std::tuple<SmallVector<LogicalTensorDesc>, bool> CustomOpDef::infer_output_attrs(
+        const SmallVector<TensorPtr> &inputs) const {
+    SmallVector<LogicalTensorDesc> input_descs(inputs.size());
+    for (int i=0; i<inputs.size(); i++) {
+        input_descs[i].comp_node = inputs[i]->comp_node();
+        input_descs[i].layout = inputs[i]->layout();
+    }
+    return std::move(this->infer_output_attrs(input_descs));
+}
+
+std::tuple<SmallVector<LogicalTensorDesc>, bool> CustomOpDef::infer_output_attrs(
+        const SmallVector<LogicalTensorDesc> &inputs) const {
+    SmallVector<CompNode> i_devices(inputs.size());
+    SmallVector<TensorShape> i_shapes(inputs.size());
+    SmallVector<megdnn::DType> i_dtypes(inputs.size());
+    SmallVector<TensorFormat> i_formats(inputs.size());
+
+    for (int i=0; i<inputs.size(); i++) {
+        i_devices[i] = inputs[i].comp_node;
+        i_shapes[i] = inputs[i].layout;   // TensorLayout is derived from TensorShape
+        i_dtypes[i] = inputs[i].layout.dtype;
+        i_formats[i] = inputs[i].layout.format;
+    }
+
+    bool success = true;
+    for (auto i_shape: i_shapes) {
+        if (i_shape.ndim == 0) {
+            success = false;
+        }
+    }
+
+    SmallVector<CompNode> o_devices;
+    SmallVector<megdnn::DType> o_dtypes;
+    SmallVector<TensorFormat> o_formats;
+    SmallVector<TensorShape> o_shapes;
+
+    o_devices = custom::to_builtin<CompNode, custom::Device>(
+        m_op->infer_output_device(
+            custom::to_custom<CompNode, custom::Device>(i_devices), this->m_param
+        )
+    );
+    o_dtypes = custom::to_builtin<megdnn::DType, custom::DType>(
+        m_op->infer_output_dtype(
+            custom::to_custom<megdnn::DType, custom::DType>(i_dtypes), this->m_param
+        )
+    );
+    o_formats = custom::to_builtin<TensorFormat, custom::Format>(
+        m_op->infer_output_format(
+            custom::to_custom<TensorFormat, custom::Format>(i_formats), this->m_param
+        )
+    );
+
+    if (success) {
+        o_shapes = custom::to_builtin<TensorShape, custom::Shape>(
+            m_op->infer_output_shape(
+                custom::to_custom<TensorShape, custom::Shape>(i_shapes), this->m_param
+            )
+        );
+    }
+    else {
+        o_shapes = SmallVector<TensorShape>(this->output_num());
+    }
+
+    SmallVector<LogicalTensorDesc> outputs(this->output_num());
+    for (int i=0; i<this->output_num(); i++) {
+        outputs[i].comp_node = std::move(o_devices[i]);
+        outputs[i].layout = std::move(
+            TensorLayout(o_shapes[i], o_dtypes[i], o_formats[i])
+        );
+    }
+    return std::tuple<SmallVector<LogicalTensorDesc>, bool>(outputs, success);
+
+}
+
+CustomOpDefFactory *CustomOpDefFactory::inst(void) {
+    static CustomOpDefFactory factory;
+    return &factory;
+}
+
+bool CustomOpDefFactory::is_custom_op(const OpDef &op) {
+    return op.dyn_typeinfo() == CustomOpDef::typeinfo();
+}
+
+CustomOpDefFactory::CustomOpDefFactory() {
+    ops = custom::CustomOpManager::inst();
+}
+
+std::vector<std::string> CustomOpDefFactory::op_list(void) const {
+    return ops->op_name_list();
+}
+
+std::shared_ptr<OpDef> CustomOpDefFactory::create_opdef(const std::string &op_type) const {
+    auto op = ops->find(op_type);
+    return std::make_shared<CustomOpDef>(op);
+}
+
+std::shared_ptr<OpDef> CustomOpDefFactory::create_opdef(const custom::RunTimeId &op_id) const {
+    auto op = ops->find(op_id);
+    return std::make_shared<CustomOpDef>(op);
+}
+
+std::shared_ptr<OpDef> CustomOpDefFactory::create_opdef(const std::string &op_type, const custom::Param &param) const {
+    auto op = ops->find(op_type);
+    return std::make_shared<CustomOpDef>(op, param);
+}
+
+std::shared_ptr<OpDef> CustomOpDefFactory::create_opdef(const custom::RunTimeId &op_id, const custom::Param &param) const {
+    auto op = ops->find(op_id);
+    return std::make_shared<CustomOpDef>(op, param);
+}
+
+namespace custom_opdef {    // avoid name conflict
+
+void apply_on_device_tensornd(const OpDef& def,
+                              const SmallVector<DeviceTensorND>& inputs,
+                              SmallVector<DeviceTensorND>* outputs) {    
+    for (auto &&output: (*outputs)) {
+        auto cn = output.comp_node();
+        cn.activate();
+    }
+    CompNode::sync_all();
+    auto&& op = static_cast<const CustomOpDef&>(def);
+    op.compute(inputs, outputs);
+
+    // for (auto &&output: (*outputs)) {
+    //     auto cn = output.comp_node();
+    //     cn.sync();       // cannot sync ??????????
+    // }
+    CompNode::sync_all();
+}
+
+SmallVector<TensorPtr> apply_on_physical_tensor(
+        const OpDef& def, const SmallVector<TensorPtr> &inputs) {
+    auto&& op = static_cast<const CustomOpDef&>(def);
+    auto [output_descs, success] = op.infer_output_attrs(inputs);
+    mgb_assert(success == true, "infer output attributes fall\n");
+    SmallVector<TensorPtr> outputs(output_descs.size());
+
+    for (size_t i=0; i<outputs.size(); ++i) {
+        auto& output = outputs[i];
+        auto& output_desc = output_descs[i];
+        output = Tensor::make(output_desc.layout, output_desc.comp_node);
+    }
+
+    SmallVector<DeviceTensorND> inp_tensornds(inputs.size());
+    SmallVector<DeviceTensorND> oup_tensornds(outputs.size());
+
+    for (size_t i = 0; i < inputs.size(); ++i)
+        inp_tensornds[i] = inputs[i]->dev_tensor();
+    for (size_t i = 0; i < outputs.size(); ++i)
+        oup_tensornds[i] = outputs[i]->dev_tensor();
+
+    apply_on_device_tensornd(def, inp_tensornds, &oup_tensornds);
+    return outputs;
+}
+
+VarNodeArray apply_on_var_node(const OpDef &def, const cg::VarNodeArray &inputs) {
+    SymbolVarArray input_syms;
+    for (auto &input_var: inputs)
+        input_syms.emplace_back(input_var);
+        
+    auto&& op = static_cast<const CustomOpDef&>(def);
+    OperatorNodeConfig config;  
+    SymbolVarArray output_syms = opr::CustomOpNode::make(
+        op.impl(), input_syms, op.param(), config
+    );
+
+    VarNodeArray outputs;
+    for (auto &output_sym: output_syms) 
+        outputs.push_back(output_sym.node());
+    return outputs;
+}
+
+std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(
+    const OpDef& def, const SmallVector<LogicalTensorDesc>& inputs) {
+    auto&& op = static_cast<const CustomOpDef&>(def);
+    return op.infer_output_attrs(inputs);
+}
+
+std::tuple<SmallVector<MemoryDesc>, SmallVector<MemoryDesc>> infer_output_mem_desc(
+    const OpDef& def,
+    const SmallVector<TensorPtr>& inputs_tensors,
+    const SmallVector<MemoryDesc>& inputs_mems) {
+    return {{}, {}};
+}
+
+size_t hash(const OpDef& def) {
+    auto&& op = static_cast<const CustomOpDef&>(def);
+    const custom::Param &param = op.param();
+    size_t val = mgb::hash(op.runtime_id());
+    std::string hash_str = "";
+    for (auto &&val: param.raw()) {
+        hash_str += val.first;
+        hash_str += val.second.str();
+    }
+
+    val = mgb::hash_pair_combine(val, mgb::hash(hash_str));
+    return val;
+}
+
+bool is_same_st(const OpDef& lhs, const OpDef& rhs) {
+    auto &&a = static_cast<const CustomOpDef&>(lhs),
+         &&b = static_cast<const CustomOpDef&>(rhs);
+    return a.param() == b.param() && a.runtime_id() == b.runtime_id();
+}
+
+std::vector<std::pair<const char*, std::string>> props(const OpDef& def) {
+    mgb_assert(false, "Custom OpDef Props Function is not IMPLEMENTED now");
+    // can be implement with param schema
+    // auto&& custom_opdef = def.cast_final_safe<CustomOpDef>();
+    std::vector<std::pair<const char*, std::string>> props_;
+    return props_;
+}
+
+std::string make_name(const OpDef& def) {
+    auto&& op = static_cast<const CustomOpDef&>(def);
+    return op.name();
+}
+
+}   // custom_opdef
+
+OP_TRAIT_REG(CustomOpDef, CustomOpDef)
+    .apply_on_physical_tensor(imperative::custom_opdef::apply_on_physical_tensor)
+    .apply_on_var_node(imperative::custom_opdef::apply_on_var_node)
+    .apply_on_device_tensornd(imperative::custom_opdef::apply_on_device_tensornd)
+    .infer_output_attrs_fallible(imperative::custom_opdef::infer_output_attrs_fallible)
+    .infer_output_mem_desc(imperative::custom_opdef::infer_output_mem_desc)
+    .hash(imperative::custom_opdef::hash)
+    .is_same_st(imperative::custom_opdef::is_same_st)
+    .props(imperative::custom_opdef::props)
+    .make_name(imperative::custom_opdef::make_name)
+    .fallback();
+
+}   // imperative
+}   // mgb
diff --git a/imperative/src/include/megbrain/imperative/ops/custom_opdef.h b/imperative/src/include/megbrain/imperative/ops/custom_opdef.h
new file mode 100644
index 0000000000000000000000000000000000000000..5f69762f73a61174edae6083bb4c1d58948ae612
--- /dev/null
+++ b/imperative/src/include/megbrain/imperative/ops/custom_opdef.h
@@ -0,0 +1,77 @@
+/**
+ * \file imperative/src/include/megbrain/imperative/ops/custom_opdef.h
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ */
+
+#pragma once
+
+#include "megbrain/custom/custom.h"
+#include "megbrain/custom/manager.h"
+#include "megbrain/imperative/op_def.h"
+
+namespace mgb {
+namespace imperative {
+
+class CustomOpDef: public OpDefImplBase<CustomOpDef> {
+    MGB_DYN_TYPE_OBJ_FINAL_DECL;
+    const std::shared_ptr<const custom::CustomOp> m_op;
+    custom::Param m_param;
+public:
+    CustomOpDef(const std::shared_ptr<const custom::CustomOp> &op);
+    CustomOpDef(const std::shared_ptr<const custom::CustomOp> &op,
+                const custom::Param&);
+
+    void param(const custom::Param&);
+    custom::Param &param(void);
+    custom::Param param(void) const;
+    size_t input_num(void) const;
+    size_t output_num(void) const;
+    std::string name(void) const;
+    custom::RunTimeId runtime_id(void) const;
+    const std::shared_ptr<const custom::CustomOp> &impl(void) const;
+
+    void compute(const SmallVector<DeviceTensorND>&, SmallVector<DeviceTensorND>*) const;
+    std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs(
+        const SmallVector<TensorPtr> &inputs) const;
+    std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs(
+        const SmallVector<LogicalTensorDesc>&) const;
+};
+
+class CustomOpDefFactory {
+    custom::CustomOpManager *ops;
+    CustomOpDefFactory();
+public:
+    PREVENT_COPY_AND_ASSIGN(CustomOpDefFactory);
+
+    static CustomOpDefFactory *inst(void);
+    static bool is_custom_op(const OpDef &op);
+
+    std::vector<std::string> op_list(void) const;
+
+    std::shared_ptr<OpDef> create_opdef(const std::string&) const;
+    std::shared_ptr<OpDef> create_opdef(const custom::RunTimeId&) const;
+    std::shared_ptr<OpDef> create_opdef(const std::string&, const custom::Param&) const;
+    std::shared_ptr<OpDef> create_opdef(const custom::RunTimeId&, const custom::Param&) const;
+};
+
+namespace custom_opdef {    // avoid name conflict
+
+void apply_on_device_tensornd(const OpDef&, const SmallVector<DeviceTensorND>&, SmallVector<DeviceTensorND>*);
+SmallVector<TensorPtr> apply_on_physical_tensor(const OpDef&, const SmallVector<TensorPtr>&);
+VarNodeArray apply_on_var_node(const OpDef&, const cg::VarNodeArray&);
+std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(const OpDef&, const SmallVector<LogicalTensorDesc>&);
+size_t hash(const OpDef&);
+bool is_same_st(const OpDef&, const OpDef&);
+std::vector<std::pair<const char*, std::string>> props(const OpDef&);
+std::string make_name(const OpDef&);
+
+}   // custom_opdef     
+
+}   // imperative
+}   // mgb