op.h

Go to the documentation of this file.

#ifndef MXNET_CPP_OP_H_
#define MXNET_CPP_OP_H_

#include <string>
#include <vector>
#include "mxnet-cpp/base.h"
#include "mxnet-cpp/shape.h"
#include "mxnet-cpp/op_util.h"
#include "mxnet-cpp/operator.h"
#include "dmlc/optional.h"
#include "nnvm/tuple.h"

namespace mxnet {
namespace cpp {

inline Symbol khatri_rao(const std::string& symbol_name,
                         const std::vector<Symbol>& args) {
  return Operator("khatri_rao")
(args)
           .CreateSymbol(symbol_name);
}

inline Symbol all_finite(const std::string& symbol_name,
                         Symbol data,
                         bool init_output = true) {
  return Operator("all_finite")
           .SetParam("init_output", init_output)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol multi_all_finite(const std::string& symbol_name,
                               const std::vector<Symbol>& data,
                               int num_arrays = 1,
                               bool init_output = true) {
  return Operator("multi_all_finite")
           .SetParam("num_arrays", num_arrays)
           .SetParam("init_output", init_output)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol Custom(const std::string& symbol_name,
                     const std::vector<Symbol>& data,
                     const std::string& op_type) {
  return Operator("Custom")
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_power(const std::string& symbol_name,
                              Symbol lhs,
                              Symbol rhs) {
  return Operator("broadcast_power")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_maximum(const std::string& symbol_name,
                                Symbol lhs,
                                Symbol rhs) {
  return Operator("broadcast_maximum")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_minimum(const std::string& symbol_name,
                                Symbol lhs,
                                Symbol rhs) {
  return Operator("broadcast_minimum")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_hypot(const std::string& symbol_name,
                              Symbol lhs,
                              Symbol rhs) {
  return Operator("broadcast_hypot")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol Reshape(const std::string& symbol_name,
                      Symbol data,
                      Shape shape = Shape(),
                      bool reverse = false,
                      Shape target_shape = Shape(),
                      bool keep_highest = false) {
  return Operator("Reshape")
           .SetParam("shape", shape)
           .SetParam("reverse", reverse)
           .SetParam("target_shape", target_shape)
           .SetParam("keep_highest", keep_highest)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol Flatten(const std::string& symbol_name,
                      Symbol data) {
  return Operator("Flatten")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol transpose(const std::string& symbol_name,
                        Symbol data,
                        Shape axes = Shape()) {
  return Operator("transpose")
           .SetParam("axes", axes)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol expand_dims(const std::string& symbol_name,
                          Symbol data,
                          int axis) {
  return Operator("expand_dims")
           .SetParam("axis", axis)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol slice(const std::string& symbol_name,
                    Symbol data,
                    Shape begin,
                    Shape end,
                    Shape step = Shape()) {
  return Operator("slice")
           .SetParam("begin", begin)
           .SetParam("end", end)
           .SetParam("step", step)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol slice_axis(const std::string& symbol_name,
                         Symbol data,
                         int axis,
                         int begin,
                         dmlc::optional<int> end) {
  return Operator("slice_axis")
           .SetParam("axis", axis)
           .SetParam("begin", begin)
           .SetParam("end", end)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol slice_like(const std::string& symbol_name,
                         Symbol data,
                         Symbol shape_like,
                         Shape axes = Shape()) {
  return Operator("slice_like")
           .SetParam("axes", axes)
           .SetInput("data", data)
           .SetInput("shape_like", shape_like)
           .CreateSymbol(symbol_name);
}

inline Symbol clip(const std::string& symbol_name,
                   Symbol data,
                   mx_float a_min,
                   mx_float a_max) {
  return Operator("clip")
           .SetParam("a_min", a_min)
           .SetParam("a_max", a_max)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol repeat(const std::string& symbol_name,
                     Symbol data,
                     int repeats,
                     dmlc::optional<int> axis = dmlc::optional<int>()) {
  return Operator("repeat")
           .SetParam("repeats", repeats)
           .SetParam("axis", axis)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol tile(const std::string& symbol_name,
                   Symbol data,
                   Shape reps) {
  return Operator("tile")
           .SetParam("reps", reps)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol reverse(const std::string& symbol_name,
                      Symbol data,
                      Shape axis) {
  return Operator("reverse")
           .SetParam("axis", axis)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol stack(const std::string& symbol_name,
                    const std::vector<Symbol>& data,
                    int num_args,
                    int axis = 0) {
  return Operator("stack")
           .SetParam("num_args", num_args)
           .SetParam("axis", axis)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol squeeze(const std::string& symbol_name,
                      const std::vector<Symbol>& data,
                      dmlc::optional<Shape> axis = dmlc::optional<Shape>()) {
  return Operator("squeeze")
           .SetParam("axis", axis)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol depth_to_space(const std::string& symbol_name,
                             Symbol data,
                             int block_size) {
  return Operator("depth_to_space")
           .SetParam("block_size", block_size)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol space_to_depth(const std::string& symbol_name,
                             Symbol data,
                             int block_size) {
  return Operator("space_to_depth")
           .SetParam("block_size", block_size)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol zeros_like(const std::string& symbol_name,
                         Symbol data) {
  return Operator("zeros_like")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol ones_like(const std::string& symbol_name,
                        Symbol data) {
  return Operator("ones_like")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol add_n(const std::string& symbol_name,
                    const std::vector<Symbol>& args) {
  return Operator("add_n")
(args)
           .CreateSymbol(symbol_name);
}

inline Symbol argmax(const std::string& symbol_name,
                     Symbol data,
                     dmlc::optional<int> axis = dmlc::optional<int>(),
                     bool keepdims = false) {
  return Operator("argmax")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol argmin(const std::string& symbol_name,
                     Symbol data,
                     dmlc::optional<int> axis = dmlc::optional<int>(),
                     bool keepdims = false) {
  return Operator("argmin")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol argmax_channel(const std::string& symbol_name,
                             Symbol data) {
  return Operator("argmax_channel")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class PickMode {
  kClip = 0,
  kWrap = 1
};

inline Symbol pick(const std::string& symbol_name,
                   Symbol data,
                   Symbol index,
                   dmlc::optional<int> axis = dmlc::optional<int>(-1),
                   bool keepdims = false,
                   PickMode mode = PickMode::kClip) {
  static const char *PickModeValues[] = {
    "clip",
    "wrap"
  };
  return Operator("pick")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("mode", PickModeValues[int(mode)])
           .SetInput("data", data)
           .SetInput("index", index)
           .CreateSymbol(symbol_name);
}

enum class DotForwardStype {
  kNone = 0,
  kCsr = 1,
  kDefault = 2,
  kRow_sparse = 3
};

inline Symbol dot(const std::string& symbol_name,
                  Symbol lhs,
                  Symbol rhs,
                  bool transpose_a = false,
                  bool transpose_b = false,
                  DotForwardStype forward_stype = DotForwardStype::kNone) {
  static const char *DotForwardStypeValues[] = {
    "None",
    "csr",
    "default",
    "row_sparse"
  };
  return Operator("dot")
           .SetParam("transpose_a", transpose_a)
           .SetParam("transpose_b", transpose_b)
           .SetParam("forward_stype", DotForwardStypeValues[int(forward_stype)])
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

enum class Batch_dotForwardStype {
  kNone = 0,
  kCsr = 1,
  kDefault = 2,
  kRow_sparse = 3
};

inline Symbol batch_dot(const std::string& symbol_name,
                        Symbol lhs,
                        Symbol rhs,
                        bool transpose_a = false,
                        bool transpose_b = false,
                        Batch_dotForwardStype forward_stype = Batch_dotForwardStype::kNone) {
  static const char *Batch_dotForwardStypeValues[] = {
    "None",
    "csr",
    "default",
    "row_sparse"
  };
  return Operator("batch_dot")
           .SetParam("transpose_a", transpose_a)
           .SetParam("transpose_b", transpose_b)
           .SetParam("forward_stype", Batch_dotForwardStypeValues[int(forward_stype)])
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_add(const std::string& symbol_name,
                            Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_add")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_sub(const std::string& symbol_name,
                            Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_sub")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_mul(const std::string& symbol_name,
                            Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_mul")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_div(const std::string& symbol_name,
                            Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_div")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_mod(const std::string& symbol_name,
                            Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_mod")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol relu(const std::string& symbol_name,
                   Symbol data) {
  return Operator("relu")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol sigmoid(const std::string& symbol_name,
                      Symbol data) {
  return Operator("sigmoid")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol hard_sigmoid(const std::string& symbol_name,
                           Symbol data,
                           mx_float alpha = 0.200000003,
                           mx_float beta = 0.5) {
  return Operator("hard_sigmoid")
           .SetParam("alpha", alpha)
           .SetParam("beta", beta)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol softsign(const std::string& symbol_name,
                       Symbol data) {
  return Operator("softsign")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol BlockGrad(const std::string& symbol_name,
                        Symbol data) {
  return Operator("BlockGrad")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol make_loss(const std::string& symbol_name,
                        Symbol data) {
  return Operator("make_loss")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol reshape_like(const std::string& symbol_name,
                           Symbol lhs,
                           Symbol rhs) {
  return Operator("reshape_like")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol shape_array(const std::string& symbol_name,
                          Symbol data,
                          dmlc::optional<int> lhs_begin = dmlc::optional<int>(),
                          dmlc::optional<int> lhs_end = dmlc::optional<int>(),
                          dmlc::optional<int> rhs_begin = dmlc::optional<int>(),
                          dmlc::optional<int> rhs_end = dmlc::optional<int>()) {
  return Operator("shape_array")
           .SetParam("lhs_begin", lhs_begin)
           .SetParam("lhs_end", lhs_end)
           .SetParam("rhs_begin", rhs_begin)
           .SetParam("rhs_end", rhs_end)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol size_array(const std::string& symbol_name,
                         Symbol data) {
  return Operator("size_array")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class CastDtype {
  kFloat16 = 0,
  kFloat32 = 1,
  kFloat64 = 2,
  kInt32 = 3,
  kInt64 = 4,
  kInt8 = 5,
  kUint8 = 6
};

inline Symbol Cast(const std::string& symbol_name,
                   Symbol data,
                   CastDtype dtype) {
  static const char *CastDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8",
    "uint8"
  };
  return Operator("Cast")
           .SetParam("dtype", CastDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol negative(const std::string& symbol_name,
                       Symbol data) {
  return Operator("negative")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol reciprocal(const std::string& symbol_name,
                         Symbol data) {
  return Operator("reciprocal")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol abs(const std::string& symbol_name,
                  Symbol data) {
  return Operator("abs")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol sign(const std::string& symbol_name,
                   Symbol data) {
  return Operator("sign")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol round(const std::string& symbol_name,
                    Symbol data) {
  return Operator("round")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol rint(const std::string& symbol_name,
                   Symbol data) {
  return Operator("rint")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol ceil(const std::string& symbol_name,
                   Symbol data) {
  return Operator("ceil")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol floor(const std::string& symbol_name,
                    Symbol data) {
  return Operator("floor")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol trunc(const std::string& symbol_name,
                    Symbol data) {
  return Operator("trunc")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol fix(const std::string& symbol_name,
                  Symbol data) {
  return Operator("fix")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol square(const std::string& symbol_name,
                     Symbol data) {
  return Operator("square")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol sqrt(const std::string& symbol_name,
                   Symbol data) {
  return Operator("sqrt")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol rsqrt(const std::string& symbol_name,
                    Symbol data) {
  return Operator("rsqrt")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol cbrt(const std::string& symbol_name,
                   Symbol data) {
  return Operator("cbrt")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol erf(const std::string& symbol_name,
                  Symbol data) {
  return Operator("erf")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol erfinv(const std::string& symbol_name,
                     Symbol data) {
  return Operator("erfinv")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol rcbrt(const std::string& symbol_name,
                    Symbol data) {
  return Operator("rcbrt")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol exp(const std::string& symbol_name,
                  Symbol data) {
  return Operator("exp")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol log(const std::string& symbol_name,
                  Symbol data) {
  return Operator("log")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol log10(const std::string& symbol_name,
                    Symbol data) {
  return Operator("log10")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol log2(const std::string& symbol_name,
                   Symbol data) {
  return Operator("log2")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol log1p(const std::string& symbol_name,
                    Symbol data) {
  return Operator("log1p")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol expm1(const std::string& symbol_name,
                    Symbol data) {
  return Operator("expm1")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol gamma(const std::string& symbol_name,
                    Symbol data) {
  return Operator("gamma")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol gammaln(const std::string& symbol_name,
                      Symbol data) {
  return Operator("gammaln")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol logical_not(const std::string& symbol_name,
                          Symbol data) {
  return Operator("logical_not")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class Amp_castDtype {
  kFloat16 = 0,
  kFloat32 = 1,
  kFloat64 = 2,
  kInt32 = 3,
  kInt64 = 4,
  kInt8 = 5,
  kUint8 = 6
};

inline Symbol amp_cast(const std::string& symbol_name,
                       Symbol data,
                       Amp_castDtype dtype) {
  static const char *Amp_castDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8",
    "uint8"
  };
  return Operator("amp_cast")
           .SetParam("dtype", Amp_castDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol amp_multicast(const std::string& symbol_name,
                            const std::vector<Symbol>& data,
                            int num_outputs) {
  return Operator("amp_multicast")
           .SetParam("num_outputs", num_outputs)
(data)
           .CreateSymbol(symbol_name);
}

enum class TopkRetTyp {
  kBoth = 0,
  kIndices = 1,
  kMask = 2,
  kValue = 3
};

enum class TopkDtype {
  kFloat16 = 0,
  kFloat32 = 1,
  kFloat64 = 2,
  kInt32 = 3,
  kUint8 = 4
};

inline Symbol topk(const std::string& symbol_name,
                   Symbol data,
                   dmlc::optional<int> axis = dmlc::optional<int>(-1),
                   int k = 1,
                   TopkRetTyp ret_typ = TopkRetTyp::kIndices,
                   bool is_ascend = false,
                   TopkDtype dtype = TopkDtype::kFloat32) {
  static const char *TopkRetTypValues[] = {
    "both",
    "indices",
    "mask",
    "value"
  };
  static const char *TopkDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "uint8"
  };
  return Operator("topk")
           .SetParam("axis", axis)
           .SetParam("k", k)
           .SetParam("ret_typ", TopkRetTypValues[int(ret_typ)])
           .SetParam("is_ascend", is_ascend)
           .SetParam("dtype", TopkDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol sort(const std::string& symbol_name,
                   Symbol data,
                   dmlc::optional<int> axis = dmlc::optional<int>(-1),
                   bool is_ascend = true) {
  return Operator("sort")
           .SetParam("axis", axis)
           .SetParam("is_ascend", is_ascend)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class ArgsortDtype {
  kFloat16 = 0,
  kFloat32 = 1,
  kFloat64 = 2,
  kInt32 = 3,
  kUint8 = 4
};

inline Symbol argsort(const std::string& symbol_name,
                      Symbol data,
                      dmlc::optional<int> axis = dmlc::optional<int>(-1),
                      bool is_ascend = true,
                      ArgsortDtype dtype = ArgsortDtype::kFloat32) {
  static const char *ArgsortDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "uint8"
  };
  return Operator("argsort")
           .SetParam("axis", axis)
           .SetParam("is_ascend", is_ascend)
           .SetParam("dtype", ArgsortDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol elemwise_add(const std::string& symbol_name,
                           Symbol lhs,
                           Symbol rhs) {
  return Operator("elemwise_add")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol elemwise_sub(const std::string& symbol_name,
                           Symbol lhs,
                           Symbol rhs) {
  return Operator("elemwise_sub")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol elemwise_mul(const std::string& symbol_name,
                           Symbol lhs,
                           Symbol rhs) {
  return Operator("elemwise_mul")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol elemwise_div(const std::string& symbol_name,
                           Symbol lhs,
                           Symbol rhs) {
  return Operator("elemwise_div")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

enum class EmbeddingDtype {
  kFloat16 = 0,
  kFloat32 = 1,
  kFloat64 = 2,
  kInt32 = 3,
  kInt64 = 4,
  kInt8 = 5,
  kUint8 = 6
};

inline Symbol Embedding(const std::string& symbol_name,
                        Symbol data,
                        Symbol weight,
                        int input_dim,
                        int output_dim,
                        EmbeddingDtype dtype = EmbeddingDtype::kFloat32,
                        bool sparse_grad = false) {
  static const char *EmbeddingDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8",
    "uint8"
  };
  return Operator("Embedding")
           .SetParam("input_dim", input_dim)
           .SetParam("output_dim", output_dim)
           .SetParam("dtype", EmbeddingDtypeValues[int(dtype)])
           .SetParam("sparse_grad", sparse_grad)
           .SetInput("data", data)
           .SetInput("weight", weight)
           .CreateSymbol(symbol_name);
}

enum class TakeMode {
  kClip = 0,
  kRaise = 1,
  kWrap = 2
};

inline Symbol take(const std::string& symbol_name,
                   Symbol a,
                   Symbol indices,
                   int axis = 0,
                   TakeMode mode = TakeMode::kClip) {
  static const char *TakeModeValues[] = {
    "clip",
    "raise",
    "wrap"
  };
  return Operator("take")
           .SetParam("axis", axis)
           .SetParam("mode", TakeModeValues[int(mode)])
           .SetInput("a", a)
           .SetInput("indices", indices)
           .CreateSymbol(symbol_name);
}

inline Symbol batch_take(const std::string& symbol_name,
                         Symbol a,
                         Symbol indices) {
  return Operator("batch_take")
           .SetInput("a", a)
           .SetInput("indices", indices)
           .CreateSymbol(symbol_name);
}

enum class One_hotDtype {
  kFloat16 = 0,
  kFloat32 = 1,
  kFloat64 = 2,
  kInt32 = 3,
  kInt64 = 4,
  kInt8 = 5,
  kUint8 = 6
};

inline Symbol one_hot(const std::string& symbol_name,
                      Symbol indices,
                      int depth,
                      double on_value = 1,
                      double off_value = 0,
                      One_hotDtype dtype = One_hotDtype::kFloat32) {
  static const char *One_hotDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8",
    "uint8"
  };
  return Operator("one_hot")
           .SetParam("depth", depth)
           .SetParam("on_value", on_value)
           .SetParam("off_value", off_value)
           .SetParam("dtype", One_hotDtypeValues[int(dtype)])
           .SetInput("indices", indices)
           .CreateSymbol(symbol_name);
}

inline Symbol gather_nd(const std::string& symbol_name,
                        Symbol data,
                        Symbol indices) {
  return Operator("gather_nd")
           .SetInput("data", data)
           .SetInput("indices", indices)
           .CreateSymbol(symbol_name);
}

inline Symbol scatter_nd(const std::string& symbol_name,
                         Symbol data,
                         Symbol indices,
                         Shape shape) {
  return Operator("scatter_nd")
           .SetParam("shape", shape)
           .SetInput("data", data)
           .SetInput("indices", indices)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_equal(const std::string& symbol_name,
                              Symbol lhs,
                              Symbol rhs) {
  return Operator("broadcast_equal")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_not_equal(const std::string& symbol_name,
                                  Symbol lhs,
                                  Symbol rhs) {
  return Operator("broadcast_not_equal")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_greater(const std::string& symbol_name,
                                Symbol lhs,
                                Symbol rhs) {
  return Operator("broadcast_greater")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_greater_equal(const std::string& symbol_name,
                                      Symbol lhs,
                                      Symbol rhs) {
  return Operator("broadcast_greater_equal")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_lesser(const std::string& symbol_name,
                               Symbol lhs,
                               Symbol rhs) {
  return Operator("broadcast_lesser")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_lesser_equal(const std::string& symbol_name,
                                     Symbol lhs,
                                     Symbol rhs) {
  return Operator("broadcast_lesser_equal")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_logical_and(const std::string& symbol_name,
                                    Symbol lhs,
                                    Symbol rhs) {
  return Operator("broadcast_logical_and")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_logical_or(const std::string& symbol_name,
                                   Symbol lhs,
                                   Symbol rhs) {
  return Operator("broadcast_logical_or")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_logical_xor(const std::string& symbol_name,
                                    Symbol lhs,
                                    Symbol rhs) {
  return Operator("broadcast_logical_xor")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol diag(const std::string& symbol_name,
                   Symbol data,
                   int k = 0,
                   int axis1 = 0,
                   int axis2 = 1) {
  return Operator("diag")
           .SetParam("k", k)
           .SetParam("axis1", axis1)
           .SetParam("axis2", axis2)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol where(const std::string& symbol_name,
                    Symbol condition,
                    Symbol x,
                    Symbol y) {
  return Operator("where")
           .SetInput("condition", condition)
           .SetInput("x", x)
           .SetInput("y", y)
           .CreateSymbol(symbol_name);
}

inline Symbol smooth_l1(const std::string& symbol_name,
                        Symbol data,
                        mx_float scalar) {
  return Operator("smooth_l1")
           .SetParam("scalar", scalar)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class Cast_storageStype {
  kCsr = 0,
  kDefault = 1,
  kRow_sparse = 2
};

inline Symbol cast_storage(const std::string& symbol_name,
                           Symbol data,
                           Cast_storageStype stype) {
  static const char *Cast_storageStypeValues[] = {
    "csr",
    "default",
    "row_sparse"
  };
  return Operator("cast_storage")
           .SetParam("stype", Cast_storageStypeValues[int(stype)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol sum(const std::string& symbol_name,
                  Symbol data,
                  dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                  bool keepdims = false,
                  bool exclude = false) {
  return Operator("sum")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol mean(const std::string& symbol_name,
                   Symbol data,
                   dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                   bool keepdims = false,
                   bool exclude = false) {
  return Operator("mean")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol prod(const std::string& symbol_name,
                   Symbol data,
                   dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                   bool keepdims = false,
                   bool exclude = false) {
  return Operator("prod")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol nansum(const std::string& symbol_name,
                     Symbol data,
                     dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                     bool keepdims = false,
                     bool exclude = false) {
  return Operator("nansum")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol nanprod(const std::string& symbol_name,
                      Symbol data,
                      dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                      bool keepdims = false,
                      bool exclude = false) {
  return Operator("nanprod")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol max(const std::string& symbol_name,
                  Symbol data,
                  dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                  bool keepdims = false,
                  bool exclude = false) {
  return Operator("max")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol min(const std::string& symbol_name,
                  Symbol data,
                  dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                  bool keepdims = false,
                  bool exclude = false) {
  return Operator("min")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_axis(const std::string& symbol_name,
                             Symbol data,
                             Shape axis = Shape(),
                             Shape size = Shape()) {
  return Operator("broadcast_axis")
           .SetParam("axis", axis)
           .SetParam("size", size)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_to(const std::string& symbol_name,
                           Symbol data,
                           Shape shape = Shape()) {
  return Operator("broadcast_to")
           .SetParam("shape", shape)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol broadcast_like(const std::string& symbol_name,
                             Symbol lhs,
                             Symbol rhs,
                             dmlc::optional<Shape> lhs_axes = dmlc::optional<Shape>(),
                             dmlc::optional<Shape> rhs_axes = dmlc::optional<Shape>()) {
  return Operator("broadcast_like")
           .SetParam("lhs_axes", lhs_axes)
           .SetParam("rhs_axes", rhs_axes)
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

enum class NormOutDtype {
  kNone = 0,
  kFloat16 = 1,
  kFloat32 = 2,
  kFloat64 = 3,
  kInt32 = 4,
  kInt64 = 5,
  kInt8 = 6
};

inline Symbol norm(const std::string& symbol_name,
                   Symbol data,
                   int ord = 2,
                   dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                   NormOutDtype out_dtype = NormOutDtype::kNone,
                   bool keepdims = false) {
  static const char *NormOutDtypeValues[] = {
    "None",
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8"
  };
  return Operator("norm")
           .SetParam("ord", ord)
           .SetParam("axis", axis)
           .SetParam("out_dtype", NormOutDtypeValues[int(out_dtype)])
           .SetParam("keepdims", keepdims)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol sin(const std::string& symbol_name,
                  Symbol data) {
  return Operator("sin")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol cos(const std::string& symbol_name,
                  Symbol data) {
  return Operator("cos")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol tan(const std::string& symbol_name,
                  Symbol data) {
  return Operator("tan")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol arcsin(const std::string& symbol_name,
                     Symbol data) {
  return Operator("arcsin")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol arccos(const std::string& symbol_name,
                     Symbol data) {
  return Operator("arccos")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol arctan(const std::string& symbol_name,
                     Symbol data) {
  return Operator("arctan")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol degrees(const std::string& symbol_name,
                      Symbol data) {
  return Operator("degrees")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol radians(const std::string& symbol_name,
                      Symbol data) {
  return Operator("radians")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol sinh(const std::string& symbol_name,
                   Symbol data) {
  return Operator("sinh")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol cosh(const std::string& symbol_name,
                   Symbol data) {
  return Operator("cosh")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol tanh(const std::string& symbol_name,
                   Symbol data) {
  return Operator("tanh")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol arcsinh(const std::string& symbol_name,
                      Symbol data) {
  return Operator("arcsinh")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol arccosh(const std::string& symbol_name,
                      Symbol data) {
  return Operator("arccosh")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol arctanh(const std::string& symbol_name,
                      Symbol data) {
  return Operator("arctanh")
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class PoolingPoolType {
  kAvg = 0,
  kLp = 1,
  kMax = 2,
  kSum = 3
};

enum class PoolingPoolingConvention {
  kFull = 0,
  kSame = 1,
  kValid = 2
};

enum class PoolingLayout {
  kNone = 0,
  kNCDHW = 1,
  kNCHW = 2,
  kNCW = 3,
  kNDHWC = 4,
  kNHWC = 5,
  kNWC = 6
};

inline Symbol Pooling(const std::string& symbol_name,
                      Symbol data,
                      Shape kernel = Shape(),
                      PoolingPoolType pool_type = PoolingPoolType::kMax,
                      bool global_pool = false,
                      bool cudnn_off = false,
                      PoolingPoolingConvention pooling_convention = PoolingPoolingConvention::kValid,
                      Shape stride = Shape(),
                      Shape pad = Shape(),
                      dmlc::optional<int> p_value = dmlc::optional<int>(),
                      dmlc::optional<bool> count_include_pad = dmlc::optional<bool>(),
                      PoolingLayout layout = PoolingLayout::kNone) {
  static const char *PoolingPoolTypeValues[] = {
    "avg",
    "lp",
    "max",
    "sum"
  };
  static const char *PoolingPoolingConventionValues[] = {
    "full",
    "same",
    "valid"
  };
  static const char *PoolingLayoutValues[] = {
    "None",
    "NCDHW",
    "NCHW",
    "NCW",
    "NDHWC",
    "NHWC",
    "NWC"
  };
  return Operator("Pooling")
           .SetParam("kernel", kernel)
           .SetParam("pool_type", PoolingPoolTypeValues[int(pool_type)])
           .SetParam("global_pool", global_pool)
           .SetParam("cudnn_off", cudnn_off)
           .SetParam("pooling_convention", PoolingPoolingConventionValues[int(pooling_convention)])
           .SetParam("stride", stride)
           .SetParam("pad", pad)
           .SetParam("p_value", p_value)
           .SetParam("count_include_pad", count_include_pad)
           .SetParam("layout", PoolingLayoutValues[int(layout)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class SoftmaxDtype {
  kNone = 0,
  kFloat16 = 1,
  kFloat32 = 2,
  kFloat64 = 3
};

inline Symbol softmax(const std::string& symbol_name,
                      Symbol data,
                      int axis = -1,
                      dmlc::optional<double> temperature = dmlc::optional<double>(),
                      SoftmaxDtype dtype = SoftmaxDtype::kNone) {
  static const char *SoftmaxDtypeValues[] = {
    "None",
    "float16",
    "float32",
    "float64"
  };
  return Operator("softmax")
           .SetParam("axis", axis)
           .SetParam("temperature", temperature)
           .SetParam("dtype", SoftmaxDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class SoftminDtype {
  kNone = 0,
  kFloat16 = 1,
  kFloat32 = 2,
  kFloat64 = 3
};

inline Symbol softmin(const std::string& symbol_name,
                      Symbol data,
                      int axis = -1,
                      dmlc::optional<double> temperature = dmlc::optional<double>(),
                      SoftminDtype dtype = SoftminDtype::kNone) {
  static const char *SoftminDtypeValues[] = {
    "None",
    "float16",
    "float32",
    "float64"
  };
  return Operator("softmin")
           .SetParam("axis", axis)
           .SetParam("temperature", temperature)
           .SetParam("dtype", SoftminDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class Log_softmaxDtype {
  kNone = 0,
  kFloat16 = 1,
  kFloat32 = 2,
  kFloat64 = 3
};

inline Symbol log_softmax(const std::string& symbol_name,
                          Symbol data,
                          int axis = -1,
                          dmlc::optional<double> temperature = dmlc::optional<double>(),
                          Log_softmaxDtype dtype = Log_softmaxDtype::kNone) {
  static const char *Log_softmaxDtypeValues[] = {
    "None",
    "float16",
    "float32",
    "float64"
  };
  return Operator("log_softmax")
           .SetParam("axis", axis)
           .SetParam("temperature", temperature)
           .SetParam("dtype", Log_softmaxDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class DeconvolutionCudnnTune {
  kNone = 0,
  kFastest = 1,
  kLimited_workspace = 2,
  kOff = 3
};

enum class DeconvolutionLayout {
  kNone = 0,
  kNCDHW = 1,
  kNCHW = 2,
  kNCW = 3,
  kNDHWC = 4,
  kNHWC = 5
};

inline Symbol Deconvolution(const std::string& symbol_name,
                            Symbol data,
                            Symbol weight,
                            Symbol bias,
                            Shape kernel,
                            uint32_t num_filter,
                            Shape stride = Shape(),
                            Shape dilate = Shape(),
                            Shape pad = Shape(),
                            Shape adj = Shape(),
                            Shape target_shape = Shape(),
                            uint32_t num_group = 1,
                            uint64_t workspace = 512,
                            bool no_bias = true,
                            DeconvolutionCudnnTune cudnn_tune = DeconvolutionCudnnTune::kNone,
                            bool cudnn_off = false,
                            DeconvolutionLayout layout = DeconvolutionLayout::kNone) {
  static const char *DeconvolutionCudnnTuneValues[] = {
    "None",
    "fastest",
    "limited_workspace",
    "off"
  };
  static const char *DeconvolutionLayoutValues[] = {
    "None",
    "NCDHW",
    "NCHW",
    "NCW",
    "NDHWC",
    "NHWC"
  };
  return Operator("Deconvolution")
           .SetParam("kernel", kernel)
           .SetParam("num_filter", num_filter)
           .SetParam("stride", stride)
           .SetParam("dilate", dilate)
           .SetParam("pad", pad)
           .SetParam("adj", adj)
           .SetParam("target_shape", target_shape)
           .SetParam("num_group", num_group)
           .SetParam("workspace", workspace)
           .SetParam("no_bias", no_bias)
           .SetParam("cudnn_tune", DeconvolutionCudnnTuneValues[int(cudnn_tune)])
           .SetParam("cudnn_off", cudnn_off)
           .SetParam("layout", DeconvolutionLayoutValues[int(layout)])
           .SetInput("data", data)
           .SetInput("weight", weight)
           .SetInput("bias", bias)
           .CreateSymbol(symbol_name);
}

enum class ActivationActType {
  kRelu = 0,
  kSigmoid = 1,
  kSoftrelu = 2,
  kSoftsign = 3,
  kTanh = 4
};

inline Symbol Activation(const std::string& symbol_name,
                         Symbol data,
                         ActivationActType act_type) {
  static const char *ActivationActTypeValues[] = {
    "relu",
    "sigmoid",
    "softrelu",
    "softsign",
    "tanh"
  };
  return Operator("Activation")
           .SetParam("act_type", ActivationActTypeValues[int(act_type)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol BatchNorm(const std::string& symbol_name,
                        Symbol data,
                        Symbol gamma,
                        Symbol beta,
                        Symbol moving_mean,
                        Symbol moving_var,
                        double eps = 0.0010000000474974513,
                        mx_float momentum = 0.899999976,
                        bool fix_gamma = true,
                        bool use_global_stats = false,
                        bool output_mean_var = false,
                        int axis = 1,
                        bool cudnn_off = false) {
  return Operator("BatchNorm")
           .SetParam("eps", eps)
           .SetParam("momentum", momentum)
           .SetParam("fix_gamma", fix_gamma)
           .SetParam("use_global_stats", use_global_stats)
           .SetParam("output_mean_var", output_mean_var)
           .SetParam("axis", axis)
           .SetParam("cudnn_off", cudnn_off)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .SetInput("beta", beta)
           .SetInput("moving_mean", moving_mean)
           .SetInput("moving_var", moving_var)
           .CreateSymbol(symbol_name);
}

enum class CTCLossBlankLabel {
  kFirst = 0,
  kLast = 1
};

inline Symbol CTCLoss(const std::string& symbol_name,
                      Symbol data,
                      Symbol label,
                      Symbol data_lengths,
                      Symbol label_lengths,
                      bool use_data_lengths = false,
                      bool use_label_lengths = false,
                      CTCLossBlankLabel blank_label = CTCLossBlankLabel::kFirst) {
  static const char *CTCLossBlankLabelValues[] = {
    "first",
    "last"
  };
  return Operator("CTCLoss")
           .SetParam("use_data_lengths", use_data_lengths)
           .SetParam("use_label_lengths", use_label_lengths)
           .SetParam("blank_label", CTCLossBlankLabelValues[int(blank_label)])
           .SetInput("data", data)
           .SetInput("label", label)
           .SetInput("data_lengths", data_lengths)
           .SetInput("label_lengths", label_lengths)
           .CreateSymbol(symbol_name);
}

inline Symbol FullyConnected(const std::string& symbol_name,
                             Symbol data,
                             Symbol weight,
                             Symbol bias,
                             int num_hidden,
                             bool no_bias = false,
                             bool flatten = true) {
  return Operator("FullyConnected")
           .SetParam("num_hidden", num_hidden)
           .SetParam("no_bias", no_bias)
           .SetParam("flatten", flatten)
           .SetInput("data", data)
           .SetInput("weight", weight)
           .SetInput("bias", bias)
           .CreateSymbol(symbol_name);
}

enum class ConvolutionCudnnTune {
  kNone = 0,
  kFastest = 1,
  kLimited_workspace = 2,
  kOff = 3
};

enum class ConvolutionLayout {
  kNone = 0,
  kNCDHW = 1,
  kNCHW = 2,
  kNCW = 3,
  kNDHWC = 4,
  kNHWC = 5
};

inline Symbol Convolution(const std::string& symbol_name,
                          Symbol data,
                          Symbol weight,
                          Symbol bias,
                          Shape kernel,
                          uint32_t num_filter,
                          Shape stride = Shape(),
                          Shape dilate = Shape(),
                          Shape pad = Shape(),
                          uint32_t num_group = 1,
                          uint64_t workspace = 1024,
                          bool no_bias = false,
                          ConvolutionCudnnTune cudnn_tune = ConvolutionCudnnTune::kNone,
                          bool cudnn_off = false,
                          ConvolutionLayout layout = ConvolutionLayout::kNone) {
  static const char *ConvolutionCudnnTuneValues[] = {
    "None",
    "fastest",
    "limited_workspace",
    "off"
  };
  static const char *ConvolutionLayoutValues[] = {
    "None",
    "NCDHW",
    "NCHW",
    "NCW",
    "NDHWC",
    "NHWC"
  };
  return Operator("Convolution")
           .SetParam("kernel", kernel)
           .SetParam("num_filter", num_filter)
           .SetParam("stride", stride)
           .SetParam("dilate", dilate)
           .SetParam("pad", pad)
           .SetParam("num_group", num_group)
           .SetParam("workspace", workspace)
           .SetParam("no_bias", no_bias)
           .SetParam("cudnn_tune", ConvolutionCudnnTuneValues[int(cudnn_tune)])
           .SetParam("cudnn_off", cudnn_off)
           .SetParam("layout", ConvolutionLayoutValues[int(layout)])
           .SetInput("data", data)
           .SetInput("weight", weight)
           .SetInput("bias", bias)
           .CreateSymbol(symbol_name);
}

enum class UpSamplingSampleType {
  kBilinear = 0,
  kNearest = 1
};

enum class UpSamplingMultiInputMode {
  kConcat = 0,
  kSum = 1
};

inline Symbol UpSampling(const std::string& symbol_name,
                         const std::vector<Symbol>& data,
                         int scale,
                         UpSamplingSampleType sample_type,
                         int num_args,
                         int num_filter = 0,
                         UpSamplingMultiInputMode multi_input_mode = UpSamplingMultiInputMode::kConcat,
                         uint64_t workspace = 512) {
  static const char *UpSamplingSampleTypeValues[] = {
    "bilinear",
    "nearest"
  };
  static const char *UpSamplingMultiInputModeValues[] = {
    "concat",
    "sum"
  };
  return Operator("UpSampling")
           .SetParam("scale", scale)
           .SetParam("sample_type", UpSamplingSampleTypeValues[int(sample_type)])
           .SetParam("num_args", num_args)
           .SetParam("num_filter", num_filter)
           .SetParam("multi_input_mode", UpSamplingMultiInputModeValues[int(multi_input_mode)])
           .SetParam("workspace", workspace)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol Concat(const std::string& symbol_name,
                     const std::vector<Symbol>& data,
                     int num_args,
                     int dim = 1) {
  return Operator("Concat")
           .SetParam("num_args", num_args)
           .SetParam("dim", dim)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol LayerNorm(const std::string& symbol_name,
                        Symbol data,
                        Symbol gamma,
                        Symbol beta,
                        int axis = -1,
                        mx_float eps = 9.99999975e-06,
                        bool output_mean_var = false) {
  return Operator("LayerNorm")
           .SetParam("axis", axis)
           .SetParam("eps", eps)
           .SetParam("output_mean_var", output_mean_var)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .SetInput("beta", beta)
           .CreateSymbol(symbol_name);
}

inline Symbol LRN(const std::string& symbol_name,
                  Symbol data,
                  uint32_t nsize,
                  mx_float alpha = 9.99999975e-05,
                  mx_float beta = 0.75,
                  mx_float knorm = 2) {
  return Operator("LRN")
           .SetParam("nsize", nsize)
           .SetParam("alpha", alpha)
           .SetParam("beta", beta)
           .SetParam("knorm", knorm)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class DropoutMode {
  kAlways = 0,
  kTraining = 1
};

inline Symbol Dropout(const std::string& symbol_name,
                      Symbol data,
                      mx_float p = 0.5,
                      DropoutMode mode = DropoutMode::kTraining,
                      Shape axes = Shape(),
                      dmlc::optional<bool> cudnn_off = dmlc::optional<bool>(0)) {
  static const char *DropoutModeValues[] = {
    "always",
    "training"
  };
  return Operator("Dropout")
           .SetParam("p", p)
           .SetParam("mode", DropoutModeValues[int(mode)])
           .SetParam("axes", axes)
           .SetParam("cudnn_off", cudnn_off)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class SoftmaxActivationMode {
  kChannel = 0,
  kInstance = 1
};

inline Symbol SoftmaxActivation(const std::string& symbol_name,
                                Symbol data,
                                SoftmaxActivationMode mode = SoftmaxActivationMode::kInstance) {
  static const char *SoftmaxActivationModeValues[] = {
    "channel",
    "instance"
  };
  return Operator("SoftmaxActivation")
           .SetParam("mode", SoftmaxActivationModeValues[int(mode)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol moments(const std::string& symbol_name,
                      Symbol data,
                      dmlc::optional<Shape> axes = dmlc::optional<Shape>(),
                      bool keepdims = false) {
  return Operator("moments")
           .SetParam("axes", axes)
           .SetParam("keepdims", keepdims)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class LeakyReLUActType {
  kElu = 0,
  kGelu = 1,
  kLeaky = 2,
  kPrelu = 3,
  kRrelu = 4,
  kSelu = 5
};

inline Symbol LeakyReLU(const std::string& symbol_name,
                        Symbol data,
                        Symbol gamma,
                        LeakyReLUActType act_type = LeakyReLUActType::kLeaky,
                        mx_float slope = 0.25,
                        mx_float lower_bound = 0.125,
                        mx_float upper_bound = 0.333999991) {
  static const char *LeakyReLUActTypeValues[] = {
    "elu",
    "gelu",
    "leaky",
    "prelu",
    "rrelu",
    "selu"
  };
  return Operator("LeakyReLU")
           .SetParam("act_type", LeakyReLUActTypeValues[int(act_type)])
           .SetParam("slope", slope)
           .SetParam("lower_bound", lower_bound)
           .SetParam("upper_bound", upper_bound)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .CreateSymbol(symbol_name);
}

enum class RNNMode {
  kGru = 0,
  kLstm = 1,
  kRnn_relu = 2,
  kRnn_tanh = 3
};

inline Symbol RNN(const std::string& symbol_name,
                  Symbol data,
                  Symbol parameters,
                  Symbol state,
                  Symbol state_cell,
                  Symbol sequence_length,
                  uint32_t state_size,
                  uint32_t num_layers,
                  RNNMode mode,
                  bool bidirectional = false,
                  mx_float p = 0,
                  bool state_outputs = false,
                  dmlc::optional<int> projection_size = dmlc::optional<int>(),
                  dmlc::optional<double> lstm_state_clip_min = dmlc::optional<double>(),
                  dmlc::optional<double> lstm_state_clip_max = dmlc::optional<double>(),
                  bool lstm_state_clip_nan = false,
                  bool use_sequence_length = false) {
  static const char *RNNModeValues[] = {
    "gru",
    "lstm",
    "rnn_relu",
    "rnn_tanh"
  };
  return Operator("RNN")
           .SetParam("state_size", state_size)
           .SetParam("num_layers", num_layers)
           .SetParam("mode", RNNModeValues[int(mode)])
           .SetParam("bidirectional", bidirectional)
           .SetParam("p", p)
           .SetParam("state_outputs", state_outputs)
           .SetParam("projection_size", projection_size)
           .SetParam("lstm_state_clip_min", lstm_state_clip_min)
           .SetParam("lstm_state_clip_max", lstm_state_clip_max)
           .SetParam("lstm_state_clip_nan", lstm_state_clip_nan)
           .SetParam("use_sequence_length", use_sequence_length)
           .SetInput("data", data)
           .SetInput("parameters", parameters)
           .SetInput("state", state)
           .SetInput("state_cell", state_cell)
           .SetInput("sequence_length", sequence_length)
           .CreateSymbol(symbol_name);
}

enum class SoftmaxOutputNormalization {
  kBatch = 0,
  kNull = 1,
  kValid = 2
};

inline Symbol SoftmaxOutput(const std::string& symbol_name,
                            Symbol data,
                            Symbol label,
                            mx_float grad_scale = 1,
                            mx_float ignore_label = -1,
                            bool multi_output = false,
                            bool use_ignore = false,
                            bool preserve_shape = false,
                            SoftmaxOutputNormalization normalization = SoftmaxOutputNormalization::kNull,
                            bool out_grad = false,
                            mx_float smooth_alpha = 0) {
  static const char *SoftmaxOutputNormalizationValues[] = {
    "batch",
    "null",
    "valid"
  };
  return Operator("SoftmaxOutput")
           .SetParam("grad_scale", grad_scale)
           .SetParam("ignore_label", ignore_label)
           .SetParam("multi_output", multi_output)
           .SetParam("use_ignore", use_ignore)
           .SetParam("preserve_shape", preserve_shape)
           .SetParam("normalization", SoftmaxOutputNormalizationValues[int(normalization)])
           .SetParam("out_grad", out_grad)
           .SetParam("smooth_alpha", smooth_alpha)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol(symbol_name);
}

inline Symbol SwapAxis(const std::string& symbol_name,
                       Symbol data,
                       uint32_t dim1 = 0,
                       uint32_t dim2 = 0) {
  return Operator("SwapAxis")
           .SetParam("dim1", dim1)
           .SetParam("dim2", dim2)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol BatchNorm_v1(const std::string& symbol_name,
                           Symbol data,
                           Symbol gamma,
                           Symbol beta,
                           mx_float eps = 0.00100000005,
                           mx_float momentum = 0.899999976,
                           bool fix_gamma = true,
                           bool use_global_stats = false,
                           bool output_mean_var = false) {
  return Operator("BatchNorm_v1")
           .SetParam("eps", eps)
           .SetParam("momentum", momentum)
           .SetParam("fix_gamma", fix_gamma)
           .SetParam("use_global_stats", use_global_stats)
           .SetParam("output_mean_var", output_mean_var)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .SetInput("beta", beta)
           .CreateSymbol(symbol_name);
}

inline Symbol softmax_cross_entropy(const std::string& symbol_name,
                                    Symbol data,
                                    Symbol label) {
  return Operator("softmax_cross_entropy")
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol(symbol_name);
}

inline Symbol LinearRegressionOutput(const std::string& symbol_name,
                                     Symbol data,
                                     Symbol label,
                                     mx_float grad_scale = 1) {
  return Operator("LinearRegressionOutput")
           .SetParam("grad_scale", grad_scale)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol(symbol_name);
}

inline Symbol MAERegressionOutput(const std::string& symbol_name,
                                  Symbol data,
                                  Symbol label,
                                  mx_float grad_scale = 1) {
  return Operator("MAERegressionOutput")
           .SetParam("grad_scale", grad_scale)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol(symbol_name);
}

inline Symbol LogisticRegressionOutput(const std::string& symbol_name,
                                       Symbol data,
                                       Symbol label,
                                       mx_float grad_scale = 1) {
  return Operator("LogisticRegressionOutput")
           .SetParam("grad_scale", grad_scale)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol(symbol_name);
}

inline Symbol IdentityAttachKLSparseReg(const std::string& symbol_name,
                                        Symbol data,
                                        mx_float sparseness_target = 0.100000001,
                                        mx_float penalty = 0.00100000005,
                                        mx_float momentum = 0.899999976) {
  return Operator("IdentityAttachKLSparseReg")
           .SetParam("sparseness_target", sparseness_target)
           .SetParam("penalty", penalty)
           .SetParam("momentum", momentum)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol signsgd_update(const std::string& symbol_name,
                             Symbol weight,
                             Symbol grad,
                             mx_float lr,
                             mx_float wd = 0,
                             mx_float rescale_grad = 1,
                             mx_float clip_gradient = -1) {
  return Operator("signsgd_update")
           .SetParam("lr", lr)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .CreateSymbol(symbol_name);
}

inline Symbol signum_update(const std::string& symbol_name,
                            Symbol weight,
                            Symbol grad,
                            Symbol mom,
                            mx_float lr,
                            mx_float momentum = 0,
                            mx_float wd = 0,
                            mx_float rescale_grad = 1,
                            mx_float clip_gradient = -1,
                            mx_float wd_lh = 0) {
  return Operator("signum_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("wd_lh", wd_lh)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .CreateSymbol(symbol_name);
}

inline Symbol multi_sgd_update(const std::string& symbol_name,
                               const std::vector<Symbol>& data,
                               nnvm::Tuple<mx_float> lrs,
                               nnvm::Tuple<mx_float> wds,
                               mx_float rescale_grad = 1,
                               mx_float clip_gradient = -1,
                               int num_weights = 1) {
  return Operator("multi_sgd_update")
           .SetParam("lrs", lrs)
           .SetParam("wds", wds)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("num_weights", num_weights)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol multi_sgd_mom_update(const std::string& symbol_name,
                                   const std::vector<Symbol>& data,
                                   nnvm::Tuple<mx_float> lrs,
                                   nnvm::Tuple<mx_float> wds,
                                   mx_float momentum = 0,
                                   mx_float rescale_grad = 1,
                                   mx_float clip_gradient = -1,
                                   int num_weights = 1) {
  return Operator("multi_sgd_mom_update")
           .SetParam("lrs", lrs)
           .SetParam("wds", wds)
           .SetParam("momentum", momentum)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("num_weights", num_weights)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol multi_mp_sgd_update(const std::string& symbol_name,
                                  const std::vector<Symbol>& data,
                                  nnvm::Tuple<mx_float> lrs,
                                  nnvm::Tuple<mx_float> wds,
                                  mx_float rescale_grad = 1,
                                  mx_float clip_gradient = -1,
                                  int num_weights = 1) {
  return Operator("multi_mp_sgd_update")
           .SetParam("lrs", lrs)
           .SetParam("wds", wds)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("num_weights", num_weights)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol multi_mp_sgd_mom_update(const std::string& symbol_name,
                                      const std::vector<Symbol>& data,
                                      nnvm::Tuple<mx_float> lrs,
                                      nnvm::Tuple<mx_float> wds,
                                      mx_float momentum = 0,
                                      mx_float rescale_grad = 1,
                                      mx_float clip_gradient = -1,
                                      int num_weights = 1) {
  return Operator("multi_mp_sgd_mom_update")
           .SetParam("lrs", lrs)
           .SetParam("wds", wds)
           .SetParam("momentum", momentum)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("num_weights", num_weights)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol sgd_update(const std::string& symbol_name,
                         Symbol weight,
                         Symbol grad,
                         mx_float lr,
                         mx_float wd = 0,
                         mx_float rescale_grad = 1,
                         mx_float clip_gradient = -1,
                         bool lazy_update = true) {
  return Operator("sgd_update")
           .SetParam("lr", lr)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .CreateSymbol(symbol_name);
}

inline Symbol sgd_mom_update(const std::string& symbol_name,
                             Symbol weight,
                             Symbol grad,
                             Symbol mom,
                             mx_float lr,
                             mx_float momentum = 0,
                             mx_float wd = 0,
                             mx_float rescale_grad = 1,
                             mx_float clip_gradient = -1,
                             bool lazy_update = true) {
  return Operator("sgd_mom_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .CreateSymbol(symbol_name);
}

inline Symbol mp_sgd_update(const std::string& symbol_name,
                            Symbol weight,
                            Symbol grad,
                            Symbol weight32,
                            mx_float lr,
                            mx_float wd = 0,
                            mx_float rescale_grad = 1,
                            mx_float clip_gradient = -1,
                            bool lazy_update = true) {
  return Operator("mp_sgd_update")
           .SetParam("lr", lr)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("weight32", weight32)
           .CreateSymbol(symbol_name);
}

inline Symbol mp_sgd_mom_update(const std::string& symbol_name,
                                Symbol weight,
                                Symbol grad,
                                Symbol mom,
                                Symbol weight32,
                                mx_float lr,
                                mx_float momentum = 0,
                                mx_float wd = 0,
                                mx_float rescale_grad = 1,
                                mx_float clip_gradient = -1,
                                bool lazy_update = true) {
  return Operator("mp_sgd_mom_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .SetInput("weight32", weight32)
           .CreateSymbol(symbol_name);
}

inline Symbol ftml_update(const std::string& symbol_name,
                          Symbol weight,
                          Symbol grad,
                          Symbol d,
                          Symbol v,
                          Symbol z,
                          mx_float lr,
                          int t,
                          mx_float beta1 = 0.600000024,
                          mx_float beta2 = 0.999000013,
                          double epsilon = 9.9999999392252903e-09,
                          mx_float wd = 0,
                          mx_float rescale_grad = 1,
                          mx_float clip_grad = -1) {
  return Operator("ftml_update")
           .SetParam("lr", lr)
           .SetParam("t", t)
           .SetParam("beta1", beta1)
           .SetParam("beta2", beta2)
           .SetParam("epsilon", epsilon)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_grad", clip_grad)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("d", d)
           .SetInput("v", v)
           .SetInput("z", z)
           .CreateSymbol(symbol_name);
}

inline Symbol adam_update(const std::string& symbol_name,
                          Symbol weight,
                          Symbol grad,
                          Symbol mean,
                          Symbol var,
                          mx_float lr,
                          mx_float beta1 = 0.899999976,
                          mx_float beta2 = 0.999000013,
                          mx_float epsilon = 9.99999994e-09,
                          mx_float wd = 0,
                          mx_float rescale_grad = 1,
                          mx_float clip_gradient = -1,
                          bool lazy_update = true) {
  return Operator("adam_update")
           .SetParam("lr", lr)
           .SetParam("beta1", beta1)
           .SetParam("beta2", beta2)
           .SetParam("epsilon", epsilon)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mean", mean)
           .SetInput("var", var)
           .CreateSymbol(symbol_name);
}

inline Symbol nag_mom_update(const std::string& symbol_name,
                             Symbol weight,
                             Symbol grad,
                             Symbol mom,
                             mx_float lr,
                             mx_float momentum = 0,
                             mx_float wd = 0,
                             mx_float rescale_grad = 1,
                             mx_float clip_gradient = -1) {
  return Operator("nag_mom_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .CreateSymbol(symbol_name);
}

inline Symbol mp_nag_mom_update(const std::string& symbol_name,
                                Symbol weight,
                                Symbol grad,
                                Symbol mom,
                                Symbol weight32,
                                mx_float lr,
                                mx_float momentum = 0,
                                mx_float wd = 0,
                                mx_float rescale_grad = 1,
                                mx_float clip_gradient = -1) {
  return Operator("mp_nag_mom_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .SetInput("weight32", weight32)
           .CreateSymbol(symbol_name);
}

inline Symbol rmsprop_update(const std::string& symbol_name,
                             Symbol weight,
                             Symbol grad,
                             Symbol n,
                             mx_float lr,
                             mx_float gamma1 = 0.949999988,
                             mx_float epsilon = 9.99999994e-09,
                             mx_float wd = 0,
                             mx_float rescale_grad = 1,
                             mx_float clip_gradient = -1,
                             mx_float clip_weights = -1) {
  return Operator("rmsprop_update")
           .SetParam("lr", lr)
           .SetParam("gamma1", gamma1)
           .SetParam("epsilon", epsilon)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("clip_weights", clip_weights)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("n", n)
           .CreateSymbol(symbol_name);
}

inline Symbol rmspropalex_update(const std::string& symbol_name,
                                 Symbol weight,
                                 Symbol grad,
                                 Symbol n,
                                 Symbol g,
                                 Symbol delta,
                                 mx_float lr,
                                 mx_float gamma1 = 0.949999988,
                                 mx_float gamma2 = 0.899999976,
                                 mx_float epsilon = 9.99999994e-09,
                                 mx_float wd = 0,
                                 mx_float rescale_grad = 1,
                                 mx_float clip_gradient = -1,
                                 mx_float clip_weights = -1) {
  return Operator("rmspropalex_update")
           .SetParam("lr", lr)
           .SetParam("gamma1", gamma1)
           .SetParam("gamma2", gamma2)
           .SetParam("epsilon", epsilon)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("clip_weights", clip_weights)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("n", n)
           .SetInput("g", g)
           .SetInput("delta", delta)
           .CreateSymbol(symbol_name);
}

inline Symbol ftrl_update(const std::string& symbol_name,
                          Symbol weight,
                          Symbol grad,
                          Symbol z,
                          Symbol n,
                          mx_float lr,
                          mx_float lamda1 = 0.00999999978,
                          mx_float beta = 1,
                          mx_float wd = 0,
                          mx_float rescale_grad = 1,
                          mx_float clip_gradient = -1) {
  return Operator("ftrl_update")
           .SetParam("lr", lr)
           .SetParam("lamda1", lamda1)
           .SetParam("beta", beta)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("z", z)
           .SetInput("n", n)
           .CreateSymbol(symbol_name);
}

inline Symbol SliceChannel(const std::string& symbol_name,
                           Symbol data,
                           int num_outputs,
                           int axis = 1,
                           bool squeeze_axis = false) {
  return Operator("SliceChannel")
           .SetParam("num_outputs", num_outputs)
           .SetParam("axis", axis)
           .SetParam("squeeze_axis", squeeze_axis)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class PadMode {
  kConstant = 0,
  kEdge = 1,
  kReflect = 2
};

inline Symbol Pad(const std::string& symbol_name,
                  Symbol data,
                  PadMode mode,
                  Shape pad_width,
                  double constant_value = 0) {
  static const char *PadModeValues[] = {
    "constant",
    "edge",
    "reflect"
  };
  return Operator("Pad")
           .SetParam("mode", PadModeValues[int(mode)])
           .SetParam("pad_width", pad_width)
           .SetParam("constant_value", constant_value)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol InstanceNorm(const std::string& symbol_name,
                           Symbol data,
                           Symbol gamma,
                           Symbol beta,
                           mx_float eps = 0.00100000005) {
  return Operator("InstanceNorm")
           .SetParam("eps", eps)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .SetInput("beta", beta)
           .CreateSymbol(symbol_name);
}

enum class GridGeneratorTransformType {
  kAffine = 0,
  kWarp = 1
};

inline Symbol GridGenerator(const std::string& symbol_name,
                            Symbol data,
                            GridGeneratorTransformType transform_type,
                            Shape target_shape = Shape(0,0)) {
  static const char *GridGeneratorTransformTypeValues[] = {
    "affine",
    "warp"
  };
  return Operator("GridGenerator")
           .SetParam("transform_type", GridGeneratorTransformTypeValues[int(transform_type)])
           .SetParam("target_shape", target_shape)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class Pooling_v1PoolType {
  kAvg = 0,
  kMax = 1,
  kSum = 2
};

enum class Pooling_v1PoolingConvention {
  kFull = 0,
  kValid = 1
};

inline Symbol Pooling_v1(const std::string& symbol_name,
                         Symbol data,
                         Shape kernel = Shape(),
                         Pooling_v1PoolType pool_type = Pooling_v1PoolType::kMax,
                         bool global_pool = false,
                         Pooling_v1PoolingConvention pooling_convention = Pooling_v1PoolingConvention::kValid,
                         Shape stride = Shape(),
                         Shape pad = Shape()) {
  static const char *Pooling_v1PoolTypeValues[] = {
    "avg",
    "max",
    "sum"
  };
  static const char *Pooling_v1PoolingConventionValues[] = {
    "full",
    "valid"
  };
  return Operator("Pooling_v1")
           .SetParam("kernel", kernel)
           .SetParam("pool_type", Pooling_v1PoolTypeValues[int(pool_type)])
           .SetParam("global_pool", global_pool)
           .SetParam("pooling_convention", Pooling_v1PoolingConventionValues[int(pooling_convention)])
           .SetParam("stride", stride)
           .SetParam("pad", pad)
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class Convolution_v1CudnnTune {
  kNone = 0,
  kFastest = 1,
  kLimited_workspace = 2,
  kOff = 3
};

enum class Convolution_v1Layout {
  kNone = 0,
  kNCDHW = 1,
  kNCHW = 2,
  kNDHWC = 3,
  kNHWC = 4
};

inline Symbol Convolution_v1(const std::string& symbol_name,
                             Symbol data,
                             Symbol weight,
                             Symbol bias,
                             Shape kernel,
                             uint32_t num_filter,
                             Shape stride = Shape(),
                             Shape dilate = Shape(),
                             Shape pad = Shape(),
                             uint32_t num_group = 1,
                             uint64_t workspace = 1024,
                             bool no_bias = false,
                             Convolution_v1CudnnTune cudnn_tune = Convolution_v1CudnnTune::kNone,
                             bool cudnn_off = false,
                             Convolution_v1Layout layout = Convolution_v1Layout::kNone) {
  static const char *Convolution_v1CudnnTuneValues[] = {
    "None",
    "fastest",
    "limited_workspace",
    "off"
  };
  static const char *Convolution_v1LayoutValues[] = {
    "None",
    "NCDHW",
    "NCHW",
    "NDHWC",
    "NHWC"
  };
  return Operator("Convolution_v1")
           .SetParam("kernel", kernel)
           .SetParam("num_filter", num_filter)
           .SetParam("stride", stride)
           .SetParam("dilate", dilate)
           .SetParam("pad", pad)
           .SetParam("num_group", num_group)
           .SetParam("workspace", workspace)
           .SetParam("no_bias", no_bias)
           .SetParam("cudnn_tune", Convolution_v1CudnnTuneValues[int(cudnn_tune)])
           .SetParam("cudnn_off", cudnn_off)
           .SetParam("layout", Convolution_v1LayoutValues[int(layout)])
           .SetInput("data", data)
           .SetInput("weight", weight)
           .SetInput("bias", bias)
           .CreateSymbol(symbol_name);
}

inline Symbol Crop(const std::string& symbol_name,
                   const std::vector<Symbol>& data,
                   int num_args,
                   Shape offset = Shape(0,0),
                   Shape h_w = Shape(0,0),
                   bool center_crop = false) {
  return Operator("Crop")
           .SetParam("num_args", num_args)
           .SetParam("offset", offset)
           .SetParam("h_w", h_w)
           .SetParam("center_crop", center_crop)
(data)
           .CreateSymbol(symbol_name);
}

inline Symbol SequenceReverse(const std::string& symbol_name,
                              Symbol data,
                              Symbol sequence_length,
                              bool use_sequence_length = false,
                              int axis = 0) {
  return Operator("SequenceReverse")
           .SetParam("use_sequence_length", use_sequence_length)
           .SetParam("axis", axis)
           .SetInput("data", data)
           .SetInput("sequence_length", sequence_length)
           .CreateSymbol(symbol_name);
}

enum class SpatialTransformerTransformType {
  kAffine = 0
};

enum class SpatialTransformerSamplerType {
  kBilinear = 0
};

inline Symbol SpatialTransformer(const std::string& symbol_name,
                                 Symbol data,
                                 Symbol loc,
                                 SpatialTransformerTransformType transform_type,
                                 SpatialTransformerSamplerType sampler_type,
                                 Shape target_shape = Shape(0,0),
                                 dmlc::optional<bool> cudnn_off = dmlc::optional<bool>()) {
  static const char *SpatialTransformerTransformTypeValues[] = {
    "affine"
  };
  static const char *SpatialTransformerSamplerTypeValues[] = {
    "bilinear"
  };
  return Operator("SpatialTransformer")
           .SetParam("transform_type", SpatialTransformerTransformTypeValues[int(transform_type)])
           .SetParam("sampler_type", SpatialTransformerSamplerTypeValues[int(sampler_type)])
           .SetParam("target_shape", target_shape)
           .SetParam("cudnn_off", cudnn_off)
           .SetInput("data", data)
           .SetInput("loc", loc)
           .CreateSymbol(symbol_name);
}

inline Symbol BilinearSampler(const std::string& symbol_name,
                              Symbol data,
                              Symbol grid,
                              dmlc::optional<bool> cudnn_off = dmlc::optional<bool>()) {
  return Operator("BilinearSampler")
           .SetParam("cudnn_off", cudnn_off)
           .SetInput("data", data)
           .SetInput("grid", grid)
           .CreateSymbol(symbol_name);
}

inline Symbol ROIPooling(const std::string& symbol_name,
                         Symbol data,
                         Symbol rois,
                         Shape pooled_size,
                         mx_float spatial_scale) {
  return Operator("ROIPooling")
           .SetParam("pooled_size", pooled_size)
           .SetParam("spatial_scale", spatial_scale)
           .SetInput("data", data)
           .SetInput("rois", rois)
           .CreateSymbol(symbol_name);
}

inline Symbol SequenceLast(const std::string& symbol_name,
                           Symbol data,
                           Symbol sequence_length,
                           bool use_sequence_length = false,
                           int axis = 0) {
  return Operator("SequenceLast")
           .SetParam("use_sequence_length", use_sequence_length)
           .SetParam("axis", axis)
           .SetInput("data", data)
           .SetInput("sequence_length", sequence_length)
           .CreateSymbol(symbol_name);
}

enum class L2NormalizationMode {
  kChannel = 0,
  kInstance = 1,
  kSpatial = 2
};

inline Symbol L2Normalization(const std::string& symbol_name,
                              Symbol data,
                              mx_float eps = 1.00000001e-10,
                              L2NormalizationMode mode = L2NormalizationMode::kInstance) {
  static const char *L2NormalizationModeValues[] = {
    "channel",
    "instance",
    "spatial"
  };
  return Operator("L2Normalization")
           .SetParam("eps", eps)
           .SetParam("mode", L2NormalizationModeValues[int(mode)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

enum class MakeLossNormalization {
  kBatch = 0,
  kNull = 1,
  kValid = 2
};

inline Symbol MakeLoss(const std::string& symbol_name,
                       Symbol data,
                       mx_float grad_scale = 1,
                       mx_float valid_thresh = 0,
                       MakeLossNormalization normalization = MakeLossNormalization::kNull) {
  static const char *MakeLossNormalizationValues[] = {
    "batch",
    "null",
    "valid"
  };
  return Operator("MakeLoss")
           .SetParam("grad_scale", grad_scale)
           .SetParam("valid_thresh", valid_thresh)
           .SetParam("normalization", MakeLossNormalizationValues[int(normalization)])
           .SetInput("data", data)
           .CreateSymbol(symbol_name);
}

inline Symbol SVMOutput(const std::string& symbol_name,
                        Symbol data,
                        Symbol label,
                        mx_float margin = 1,
                        mx_float regularization_coefficient = 1,
                        bool use_linear = false) {
  return Operator("SVMOutput")
           .SetParam("margin", margin)
           .SetParam("regularization_coefficient", regularization_coefficient)
           .SetParam("use_linear", use_linear)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol(symbol_name);
}

inline Symbol Correlation(const std::string& symbol_name,
                          Symbol data1,
                          Symbol data2,
                          uint32_t kernel_size = 1,
                          uint32_t max_displacement = 1,
                          uint32_t stride1 = 1,
                          uint32_t stride2 = 1,
                          uint32_t pad_size = 0,
                          bool is_multiply = true) {
  return Operator("Correlation")
           .SetParam("kernel_size", kernel_size)
           .SetParam("max_displacement", max_displacement)
           .SetParam("stride1", stride1)
           .SetParam("stride2", stride2)
           .SetParam("pad_size", pad_size)
           .SetParam("is_multiply", is_multiply)
           .SetInput("data1", data1)
           .SetInput("data2", data2)
           .CreateSymbol(symbol_name);
}

inline Symbol SequenceMask(const std::string& symbol_name,
                           Symbol data,
                           Symbol sequence_length,
                           bool use_sequence_length = false,
                           mx_float value = 0,
                           int axis = 0) {
  return Operator("SequenceMask")
           .SetParam("use_sequence_length", use_sequence_length)
           .SetParam("value", value)
           .SetParam("axis", axis)
           .SetInput("data", data)
           .SetInput("sequence_length", sequence_length)
           .CreateSymbol(symbol_name);
}

inline Symbol fill_element_0index(const std::string& symbol_name,
                                  Symbol lhs,
                                  Symbol mhs,
                                  Symbol rhs) {
  return Operator("fill_element_0index")
           .SetInput("lhs", lhs)
           .SetInput("mhs", mhs)
           .SetInput("rhs", rhs)
           .CreateSymbol(symbol_name);
}

inline Symbol khatri_rao(const std::vector<Symbol>& args) {
  return Operator("khatri_rao")
(args)
           .CreateSymbol();
}

inline Symbol all_finite(Symbol data,
                         bool init_output = true) {
  return Operator("all_finite")
           .SetParam("init_output", init_output)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol multi_all_finite(const std::vector<Symbol>& data,
                               int num_arrays = 1,
                               bool init_output = true) {
  return Operator("multi_all_finite")
           .SetParam("num_arrays", num_arrays)
           .SetParam("init_output", init_output)
(data)
           .CreateSymbol();
}

inline Symbol Custom(const std::vector<Symbol>& data,
                     const std::string& op_type) {
  return Operator("Custom")
(data)
           .CreateSymbol();
}

inline Symbol broadcast_power(Symbol lhs,
                              Symbol rhs) {
  return Operator("broadcast_power")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_maximum(Symbol lhs,
                                Symbol rhs) {
  return Operator("broadcast_maximum")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_minimum(Symbol lhs,
                                Symbol rhs) {
  return Operator("broadcast_minimum")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_hypot(Symbol lhs,
                              Symbol rhs) {
  return Operator("broadcast_hypot")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol Reshape(Symbol data,
                      Shape shape = Shape(),
                      bool reverse = false,
                      Shape target_shape = Shape(),
                      bool keep_highest = false) {
  return Operator("Reshape")
           .SetParam("shape", shape)
           .SetParam("reverse", reverse)
           .SetParam("target_shape", target_shape)
           .SetParam("keep_highest", keep_highest)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol Flatten(Symbol data) {
  return Operator("Flatten")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol transpose(Symbol data,
                        Shape axes = Shape()) {
  return Operator("transpose")
           .SetParam("axes", axes)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol expand_dims(Symbol data,
                          int axis) {
  return Operator("expand_dims")
           .SetParam("axis", axis)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol slice(Symbol data,
                    Shape begin,
                    Shape end,
                    Shape step = Shape()) {
  return Operator("slice")
           .SetParam("begin", begin)
           .SetParam("end", end)
           .SetParam("step", step)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol slice_axis(Symbol data,
                         int axis,
                         int begin,
                         dmlc::optional<int> end) {
  return Operator("slice_axis")
           .SetParam("axis", axis)
           .SetParam("begin", begin)
           .SetParam("end", end)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol slice_like(Symbol data,
                         Symbol shape_like,
                         Shape axes = Shape()) {
  return Operator("slice_like")
           .SetParam("axes", axes)
           .SetInput("data", data)
           .SetInput("shape_like", shape_like)
           .CreateSymbol();
}

inline Symbol clip(Symbol data,
                   mx_float a_min,
                   mx_float a_max) {
  return Operator("clip")
           .SetParam("a_min", a_min)
           .SetParam("a_max", a_max)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol repeat(Symbol data,
                     int repeats,
                     dmlc::optional<int> axis = dmlc::optional<int>()) {
  return Operator("repeat")
           .SetParam("repeats", repeats)
           .SetParam("axis", axis)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol tile(Symbol data,
                   Shape reps) {
  return Operator("tile")
           .SetParam("reps", reps)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol reverse(Symbol data,
                      Shape axis) {
  return Operator("reverse")
           .SetParam("axis", axis)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol stack(const std::vector<Symbol>& data,
                    int num_args,
                    int axis = 0) {
  return Operator("stack")
           .SetParam("num_args", num_args)
           .SetParam("axis", axis)
(data)
           .CreateSymbol();
}

inline Symbol squeeze(const std::vector<Symbol>& data,
                      dmlc::optional<Shape> axis = dmlc::optional<Shape>()) {
  return Operator("squeeze")
           .SetParam("axis", axis)
(data)
           .CreateSymbol();
}

inline Symbol depth_to_space(Symbol data,
                             int block_size) {
  return Operator("depth_to_space")
           .SetParam("block_size", block_size)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol space_to_depth(Symbol data,
                             int block_size) {
  return Operator("space_to_depth")
           .SetParam("block_size", block_size)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol zeros_like(Symbol data) {
  return Operator("zeros_like")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol ones_like(Symbol data) {
  return Operator("ones_like")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol add_n(const std::vector<Symbol>& args) {
  return Operator("add_n")
(args)
           .CreateSymbol();
}

inline Symbol argmax(Symbol data,
                     dmlc::optional<int> axis = dmlc::optional<int>(),
                     bool keepdims = false) {
  return Operator("argmax")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol argmin(Symbol data,
                     dmlc::optional<int> axis = dmlc::optional<int>(),
                     bool keepdims = false) {
  return Operator("argmin")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol argmax_channel(Symbol data) {
  return Operator("argmax_channel")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol pick(Symbol data,
                   Symbol index,
                   dmlc::optional<int> axis = dmlc::optional<int>(-1),
                   bool keepdims = false,
                   PickMode mode = PickMode::kClip) {
  static const char *PickModeValues[] = {
    "clip",
    "wrap"
  };
  return Operator("pick")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("mode", PickModeValues[int(mode)])
           .SetInput("data", data)
           .SetInput("index", index)
           .CreateSymbol();
}

inline Symbol dot(Symbol lhs,
                  Symbol rhs,
                  bool transpose_a = false,
                  bool transpose_b = false,
                  DotForwardStype forward_stype = DotForwardStype::kNone) {
  static const char *DotForwardStypeValues[] = {
    "None",
    "csr",
    "default",
    "row_sparse"
  };
  return Operator("dot")
           .SetParam("transpose_a", transpose_a)
           .SetParam("transpose_b", transpose_b)
           .SetParam("forward_stype", DotForwardStypeValues[int(forward_stype)])
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol batch_dot(Symbol lhs,
                        Symbol rhs,
                        bool transpose_a = false,
                        bool transpose_b = false,
                        Batch_dotForwardStype forward_stype = Batch_dotForwardStype::kNone) {
  static const char *Batch_dotForwardStypeValues[] = {
    "None",
    "csr",
    "default",
    "row_sparse"
  };
  return Operator("batch_dot")
           .SetParam("transpose_a", transpose_a)
           .SetParam("transpose_b", transpose_b)
           .SetParam("forward_stype", Batch_dotForwardStypeValues[int(forward_stype)])
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_add(Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_add")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_sub(Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_sub")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_mul(Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_mul")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_div(Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_div")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_mod(Symbol lhs,
                            Symbol rhs) {
  return Operator("broadcast_mod")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol relu(Symbol data) {
  return Operator("relu")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol sigmoid(Symbol data) {
  return Operator("sigmoid")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol hard_sigmoid(Symbol data,
                           mx_float alpha = 0.200000003,
                           mx_float beta = 0.5) {
  return Operator("hard_sigmoid")
           .SetParam("alpha", alpha)
           .SetParam("beta", beta)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol softsign(Symbol data) {
  return Operator("softsign")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol BlockGrad(Symbol data) {
  return Operator("BlockGrad")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol make_loss(Symbol data) {
  return Operator("make_loss")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol reshape_like(Symbol lhs,
                           Symbol rhs) {
  return Operator("reshape_like")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol shape_array(Symbol data,
                          dmlc::optional<int> lhs_begin = dmlc::optional<int>(),
                          dmlc::optional<int> lhs_end = dmlc::optional<int>(),
                          dmlc::optional<int> rhs_begin = dmlc::optional<int>(),
                          dmlc::optional<int> rhs_end = dmlc::optional<int>()) {
  return Operator("shape_array")
           .SetParam("lhs_begin", lhs_begin)
           .SetParam("lhs_end", lhs_end)
           .SetParam("rhs_begin", rhs_begin)
           .SetParam("rhs_end", rhs_end)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol size_array(Symbol data) {
  return Operator("size_array")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol Cast(Symbol data,
                   CastDtype dtype) {
  static const char *CastDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8",
    "uint8"
  };
  return Operator("Cast")
           .SetParam("dtype", CastDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol negative(Symbol data) {
  return Operator("negative")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol reciprocal(Symbol data) {
  return Operator("reciprocal")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol abs(Symbol data) {
  return Operator("abs")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol sign(Symbol data) {
  return Operator("sign")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol round(Symbol data) {
  return Operator("round")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol rint(Symbol data) {
  return Operator("rint")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol ceil(Symbol data) {
  return Operator("ceil")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol floor(Symbol data) {
  return Operator("floor")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol trunc(Symbol data) {
  return Operator("trunc")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol fix(Symbol data) {
  return Operator("fix")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol square(Symbol data) {
  return Operator("square")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol sqrt(Symbol data) {
  return Operator("sqrt")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol rsqrt(Symbol data) {
  return Operator("rsqrt")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol cbrt(Symbol data) {
  return Operator("cbrt")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol erf(Symbol data) {
  return Operator("erf")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol erfinv(Symbol data) {
  return Operator("erfinv")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol rcbrt(Symbol data) {
  return Operator("rcbrt")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol exp(Symbol data) {
  return Operator("exp")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol log(Symbol data) {
  return Operator("log")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol log10(Symbol data) {
  return Operator("log10")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol log2(Symbol data) {
  return Operator("log2")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol log1p(Symbol data) {
  return Operator("log1p")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol expm1(Symbol data) {
  return Operator("expm1")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol gamma(Symbol data) {
  return Operator("gamma")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol gammaln(Symbol data) {
  return Operator("gammaln")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol logical_not(Symbol data) {
  return Operator("logical_not")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol amp_cast(Symbol data,
                       Amp_castDtype dtype) {
  static const char *Amp_castDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8",
    "uint8"
  };
  return Operator("amp_cast")
           .SetParam("dtype", Amp_castDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol amp_multicast(const std::vector<Symbol>& data,
                            int num_outputs) {
  return Operator("amp_multicast")
           .SetParam("num_outputs", num_outputs)
(data)
           .CreateSymbol();
}

inline Symbol topk(Symbol data,
                   dmlc::optional<int> axis = dmlc::optional<int>(-1),
                   int k = 1,
                   TopkRetTyp ret_typ = TopkRetTyp::kIndices,
                   bool is_ascend = false,
                   TopkDtype dtype = TopkDtype::kFloat32) {
  static const char *TopkRetTypValues[] = {
    "both",
    "indices",
    "mask",
    "value"
  };
  static const char *TopkDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "uint8"
  };
  return Operator("topk")
           .SetParam("axis", axis)
           .SetParam("k", k)
           .SetParam("ret_typ", TopkRetTypValues[int(ret_typ)])
           .SetParam("is_ascend", is_ascend)
           .SetParam("dtype", TopkDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol sort(Symbol data,
                   dmlc::optional<int> axis = dmlc::optional<int>(-1),
                   bool is_ascend = true) {
  return Operator("sort")
           .SetParam("axis", axis)
           .SetParam("is_ascend", is_ascend)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol argsort(Symbol data,
                      dmlc::optional<int> axis = dmlc::optional<int>(-1),
                      bool is_ascend = true,
                      ArgsortDtype dtype = ArgsortDtype::kFloat32) {
  static const char *ArgsortDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "uint8"
  };
  return Operator("argsort")
           .SetParam("axis", axis)
           .SetParam("is_ascend", is_ascend)
           .SetParam("dtype", ArgsortDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol elemwise_add(Symbol lhs,
                           Symbol rhs) {
  return Operator("elemwise_add")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol elemwise_sub(Symbol lhs,
                           Symbol rhs) {
  return Operator("elemwise_sub")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol elemwise_mul(Symbol lhs,
                           Symbol rhs) {
  return Operator("elemwise_mul")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol elemwise_div(Symbol lhs,
                           Symbol rhs) {
  return Operator("elemwise_div")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol Embedding(Symbol data,
                        Symbol weight,
                        int input_dim,
                        int output_dim,
                        EmbeddingDtype dtype = EmbeddingDtype::kFloat32,
                        bool sparse_grad = false) {
  static const char *EmbeddingDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8",
    "uint8"
  };
  return Operator("Embedding")
           .SetParam("input_dim", input_dim)
           .SetParam("output_dim", output_dim)
           .SetParam("dtype", EmbeddingDtypeValues[int(dtype)])
           .SetParam("sparse_grad", sparse_grad)
           .SetInput("data", data)
           .SetInput("weight", weight)
           .CreateSymbol();
}

inline Symbol take(Symbol a,
                   Symbol indices,
                   int axis = 0,
                   TakeMode mode = TakeMode::kClip) {
  static const char *TakeModeValues[] = {
    "clip",
    "raise",
    "wrap"
  };
  return Operator("take")
           .SetParam("axis", axis)
           .SetParam("mode", TakeModeValues[int(mode)])
           .SetInput("a", a)
           .SetInput("indices", indices)
           .CreateSymbol();
}

inline Symbol batch_take(Symbol a,
                         Symbol indices) {
  return Operator("batch_take")
           .SetInput("a", a)
           .SetInput("indices", indices)
           .CreateSymbol();
}

inline Symbol one_hot(Symbol indices,
                      int depth,
                      double on_value = 1,
                      double off_value = 0,
                      One_hotDtype dtype = One_hotDtype::kFloat32) {
  static const char *One_hotDtypeValues[] = {
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8",
    "uint8"
  };
  return Operator("one_hot")
           .SetParam("depth", depth)
           .SetParam("on_value", on_value)
           .SetParam("off_value", off_value)
           .SetParam("dtype", One_hotDtypeValues[int(dtype)])
           .SetInput("indices", indices)
           .CreateSymbol();
}

inline Symbol gather_nd(Symbol data,
                        Symbol indices) {
  return Operator("gather_nd")
           .SetInput("data", data)
           .SetInput("indices", indices)
           .CreateSymbol();
}

inline Symbol scatter_nd(Symbol data,
                         Symbol indices,
                         Shape shape) {
  return Operator("scatter_nd")
           .SetParam("shape", shape)
           .SetInput("data", data)
           .SetInput("indices", indices)
           .CreateSymbol();
}

inline Symbol broadcast_equal(Symbol lhs,
                              Symbol rhs) {
  return Operator("broadcast_equal")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_not_equal(Symbol lhs,
                                  Symbol rhs) {
  return Operator("broadcast_not_equal")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_greater(Symbol lhs,
                                Symbol rhs) {
  return Operator("broadcast_greater")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_greater_equal(Symbol lhs,
                                      Symbol rhs) {
  return Operator("broadcast_greater_equal")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_lesser(Symbol lhs,
                               Symbol rhs) {
  return Operator("broadcast_lesser")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_lesser_equal(Symbol lhs,
                                     Symbol rhs) {
  return Operator("broadcast_lesser_equal")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_logical_and(Symbol lhs,
                                    Symbol rhs) {
  return Operator("broadcast_logical_and")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_logical_or(Symbol lhs,
                                   Symbol rhs) {
  return Operator("broadcast_logical_or")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol broadcast_logical_xor(Symbol lhs,
                                    Symbol rhs) {
  return Operator("broadcast_logical_xor")
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol diag(Symbol data,
                   int k = 0,
                   int axis1 = 0,
                   int axis2 = 1) {
  return Operator("diag")
           .SetParam("k", k)
           .SetParam("axis1", axis1)
           .SetParam("axis2", axis2)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol where(Symbol condition,
                    Symbol x,
                    Symbol y) {
  return Operator("where")
           .SetInput("condition", condition)
           .SetInput("x", x)
           .SetInput("y", y)
           .CreateSymbol();
}

inline Symbol smooth_l1(Symbol data,
                        mx_float scalar) {
  return Operator("smooth_l1")
           .SetParam("scalar", scalar)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol cast_storage(Symbol data,
                           Cast_storageStype stype) {
  static const char *Cast_storageStypeValues[] = {
    "csr",
    "default",
    "row_sparse"
  };
  return Operator("cast_storage")
           .SetParam("stype", Cast_storageStypeValues[int(stype)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol sum(Symbol data,
                  dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                  bool keepdims = false,
                  bool exclude = false) {
  return Operator("sum")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol mean(Symbol data,
                   dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                   bool keepdims = false,
                   bool exclude = false) {
  return Operator("mean")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol prod(Symbol data,
                   dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                   bool keepdims = false,
                   bool exclude = false) {
  return Operator("prod")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol nansum(Symbol data,
                     dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                     bool keepdims = false,
                     bool exclude = false) {
  return Operator("nansum")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol nanprod(Symbol data,
                      dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                      bool keepdims = false,
                      bool exclude = false) {
  return Operator("nanprod")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol max(Symbol data,
                  dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                  bool keepdims = false,
                  bool exclude = false) {
  return Operator("max")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol min(Symbol data,
                  dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                  bool keepdims = false,
                  bool exclude = false) {
  return Operator("min")
           .SetParam("axis", axis)
           .SetParam("keepdims", keepdims)
           .SetParam("exclude", exclude)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol broadcast_axis(Symbol data,
                             Shape axis = Shape(),
                             Shape size = Shape()) {
  return Operator("broadcast_axis")
           .SetParam("axis", axis)
           .SetParam("size", size)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol broadcast_to(Symbol data,
                           Shape shape = Shape()) {
  return Operator("broadcast_to")
           .SetParam("shape", shape)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol broadcast_like(Symbol lhs,
                             Symbol rhs,
                             dmlc::optional<Shape> lhs_axes = dmlc::optional<Shape>(),
                             dmlc::optional<Shape> rhs_axes = dmlc::optional<Shape>()) {
  return Operator("broadcast_like")
           .SetParam("lhs_axes", lhs_axes)
           .SetParam("rhs_axes", rhs_axes)
           .SetInput("lhs", lhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

inline Symbol norm(Symbol data,
                   int ord = 2,
                   dmlc::optional<Shape> axis = dmlc::optional<Shape>(),
                   NormOutDtype out_dtype = NormOutDtype::kNone,
                   bool keepdims = false) {
  static const char *NormOutDtypeValues[] = {
    "None",
    "float16",
    "float32",
    "float64",
    "int32",
    "int64",
    "int8"
  };
  return Operator("norm")
           .SetParam("ord", ord)
           .SetParam("axis", axis)
           .SetParam("out_dtype", NormOutDtypeValues[int(out_dtype)])
           .SetParam("keepdims", keepdims)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol sin(Symbol data) {
  return Operator("sin")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol cos(Symbol data) {
  return Operator("cos")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol tan(Symbol data) {
  return Operator("tan")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol arcsin(Symbol data) {
  return Operator("arcsin")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol arccos(Symbol data) {
  return Operator("arccos")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol arctan(Symbol data) {
  return Operator("arctan")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol degrees(Symbol data) {
  return Operator("degrees")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol radians(Symbol data) {
  return Operator("radians")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol sinh(Symbol data) {
  return Operator("sinh")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol cosh(Symbol data) {
  return Operator("cosh")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol tanh(Symbol data) {
  return Operator("tanh")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol arcsinh(Symbol data) {
  return Operator("arcsinh")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol arccosh(Symbol data) {
  return Operator("arccosh")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol arctanh(Symbol data) {
  return Operator("arctanh")
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol Pooling(Symbol data,
                      Shape kernel = Shape(),
                      PoolingPoolType pool_type = PoolingPoolType::kMax,
                      bool global_pool = false,
                      bool cudnn_off = false,
                      PoolingPoolingConvention pooling_convention = PoolingPoolingConvention::kValid,
                      Shape stride = Shape(),
                      Shape pad = Shape(),
                      dmlc::optional<int> p_value = dmlc::optional<int>(),
                      dmlc::optional<bool> count_include_pad = dmlc::optional<bool>(),
                      PoolingLayout layout = PoolingLayout::kNone) {
  static const char *PoolingPoolTypeValues[] = {
    "avg",
    "lp",
    "max",
    "sum"
  };
  static const char *PoolingPoolingConventionValues[] = {
    "full",
    "same",
    "valid"
  };
  static const char *PoolingLayoutValues[] = {
    "None",
    "NCDHW",
    "NCHW",
    "NCW",
    "NDHWC",
    "NHWC",
    "NWC"
  };
  return Operator("Pooling")
           .SetParam("kernel", kernel)
           .SetParam("pool_type", PoolingPoolTypeValues[int(pool_type)])
           .SetParam("global_pool", global_pool)
           .SetParam("cudnn_off", cudnn_off)
           .SetParam("pooling_convention", PoolingPoolingConventionValues[int(pooling_convention)])
           .SetParam("stride", stride)
           .SetParam("pad", pad)
           .SetParam("p_value", p_value)
           .SetParam("count_include_pad", count_include_pad)
           .SetParam("layout", PoolingLayoutValues[int(layout)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol softmax(Symbol data,
                      int axis = -1,
                      dmlc::optional<double> temperature = dmlc::optional<double>(),
                      SoftmaxDtype dtype = SoftmaxDtype::kNone) {
  static const char *SoftmaxDtypeValues[] = {
    "None",
    "float16",
    "float32",
    "float64"
  };
  return Operator("softmax")
           .SetParam("axis", axis)
           .SetParam("temperature", temperature)
           .SetParam("dtype", SoftmaxDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol softmin(Symbol data,
                      int axis = -1,
                      dmlc::optional<double> temperature = dmlc::optional<double>(),
                      SoftminDtype dtype = SoftminDtype::kNone) {
  static const char *SoftminDtypeValues[] = {
    "None",
    "float16",
    "float32",
    "float64"
  };
  return Operator("softmin")
           .SetParam("axis", axis)
           .SetParam("temperature", temperature)
           .SetParam("dtype", SoftminDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol log_softmax(Symbol data,
                          int axis = -1,
                          dmlc::optional<double> temperature = dmlc::optional<double>(),
                          Log_softmaxDtype dtype = Log_softmaxDtype::kNone) {
  static const char *Log_softmaxDtypeValues[] = {
    "None",
    "float16",
    "float32",
    "float64"
  };
  return Operator("log_softmax")
           .SetParam("axis", axis)
           .SetParam("temperature", temperature)
           .SetParam("dtype", Log_softmaxDtypeValues[int(dtype)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol Deconvolution(Symbol data,
                            Symbol weight,
                            Symbol bias,
                            Shape kernel,
                            uint32_t num_filter,
                            Shape stride = Shape(),
                            Shape dilate = Shape(),
                            Shape pad = Shape(),
                            Shape adj = Shape(),
                            Shape target_shape = Shape(),
                            uint32_t num_group = 1,
                            uint64_t workspace = 512,
                            bool no_bias = true,
                            DeconvolutionCudnnTune cudnn_tune = DeconvolutionCudnnTune::kNone,
                            bool cudnn_off = false,
                            DeconvolutionLayout layout = DeconvolutionLayout::kNone) {
  static const char *DeconvolutionCudnnTuneValues[] = {
    "None",
    "fastest",
    "limited_workspace",
    "off"
  };
  static const char *DeconvolutionLayoutValues[] = {
    "None",
    "NCDHW",
    "NCHW",
    "NCW",
    "NDHWC",
    "NHWC"
  };
  return Operator("Deconvolution")
           .SetParam("kernel", kernel)
           .SetParam("num_filter", num_filter)
           .SetParam("stride", stride)
           .SetParam("dilate", dilate)
           .SetParam("pad", pad)
           .SetParam("adj", adj)
           .SetParam("target_shape", target_shape)
           .SetParam("num_group", num_group)
           .SetParam("workspace", workspace)
           .SetParam("no_bias", no_bias)
           .SetParam("cudnn_tune", DeconvolutionCudnnTuneValues[int(cudnn_tune)])
           .SetParam("cudnn_off", cudnn_off)
           .SetParam("layout", DeconvolutionLayoutValues[int(layout)])
           .SetInput("data", data)
           .SetInput("weight", weight)
           .SetInput("bias", bias)
           .CreateSymbol();
}

inline Symbol Activation(Symbol data,
                         ActivationActType act_type) {
  static const char *ActivationActTypeValues[] = {
    "relu",
    "sigmoid",
    "softrelu",
    "softsign",
    "tanh"
  };
  return Operator("Activation")
           .SetParam("act_type", ActivationActTypeValues[int(act_type)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol BatchNorm(Symbol data,
                        Symbol gamma,
                        Symbol beta,
                        Symbol moving_mean,
                        Symbol moving_var,
                        double eps = 0.0010000000474974513,
                        mx_float momentum = 0.899999976,
                        bool fix_gamma = true,
                        bool use_global_stats = false,
                        bool output_mean_var = false,
                        int axis = 1,
                        bool cudnn_off = false) {
  return Operator("BatchNorm")
           .SetParam("eps", eps)
           .SetParam("momentum", momentum)
           .SetParam("fix_gamma", fix_gamma)
           .SetParam("use_global_stats", use_global_stats)
           .SetParam("output_mean_var", output_mean_var)
           .SetParam("axis", axis)
           .SetParam("cudnn_off", cudnn_off)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .SetInput("beta", beta)
           .SetInput("moving_mean", moving_mean)
           .SetInput("moving_var", moving_var)
           .CreateSymbol();
}

inline Symbol CTCLoss(Symbol data,
                      Symbol label,
                      Symbol data_lengths,
                      Symbol label_lengths,
                      bool use_data_lengths = false,
                      bool use_label_lengths = false,
                      CTCLossBlankLabel blank_label = CTCLossBlankLabel::kFirst) {
  static const char *CTCLossBlankLabelValues[] = {
    "first",
    "last"
  };
  return Operator("CTCLoss")
           .SetParam("use_data_lengths", use_data_lengths)
           .SetParam("use_label_lengths", use_label_lengths)
           .SetParam("blank_label", CTCLossBlankLabelValues[int(blank_label)])
           .SetInput("data", data)
           .SetInput("label", label)
           .SetInput("data_lengths", data_lengths)
           .SetInput("label_lengths", label_lengths)
           .CreateSymbol();
}

inline Symbol FullyConnected(Symbol data,
                             Symbol weight,
                             Symbol bias,
                             int num_hidden,
                             bool no_bias = false,
                             bool flatten = true) {
  return Operator("FullyConnected")
           .SetParam("num_hidden", num_hidden)
           .SetParam("no_bias", no_bias)
           .SetParam("flatten", flatten)
           .SetInput("data", data)
           .SetInput("weight", weight)
           .SetInput("bias", bias)
           .CreateSymbol();
}

inline Symbol Convolution(Symbol data,
                          Symbol weight,
                          Symbol bias,
                          Shape kernel,
                          uint32_t num_filter,
                          Shape stride = Shape(),
                          Shape dilate = Shape(),
                          Shape pad = Shape(),
                          uint32_t num_group = 1,
                          uint64_t workspace = 1024,
                          bool no_bias = false,
                          ConvolutionCudnnTune cudnn_tune = ConvolutionCudnnTune::kNone,
                          bool cudnn_off = false,
                          ConvolutionLayout layout = ConvolutionLayout::kNone) {
  static const char *ConvolutionCudnnTuneValues[] = {
    "None",
    "fastest",
    "limited_workspace",
    "off"
  };
  static const char *ConvolutionLayoutValues[] = {
    "None",
    "NCDHW",
    "NCHW",
    "NCW",
    "NDHWC",
    "NHWC"
  };
  return Operator("Convolution")
           .SetParam("kernel", kernel)
           .SetParam("num_filter", num_filter)
           .SetParam("stride", stride)
           .SetParam("dilate", dilate)
           .SetParam("pad", pad)
           .SetParam("num_group", num_group)
           .SetParam("workspace", workspace)
           .SetParam("no_bias", no_bias)
           .SetParam("cudnn_tune", ConvolutionCudnnTuneValues[int(cudnn_tune)])
           .SetParam("cudnn_off", cudnn_off)
           .SetParam("layout", ConvolutionLayoutValues[int(layout)])
           .SetInput("data", data)
           .SetInput("weight", weight)
           .SetInput("bias", bias)
           .CreateSymbol();
}

inline Symbol UpSampling(const std::vector<Symbol>& data,
                         int scale,
                         UpSamplingSampleType sample_type,
                         int num_args,
                         int num_filter = 0,
                         UpSamplingMultiInputMode multi_input_mode = UpSamplingMultiInputMode::kConcat,
                         uint64_t workspace = 512) {
  static const char *UpSamplingSampleTypeValues[] = {
    "bilinear",
    "nearest"
  };
  static const char *UpSamplingMultiInputModeValues[] = {
    "concat",
    "sum"
  };
  return Operator("UpSampling")
           .SetParam("scale", scale)
           .SetParam("sample_type", UpSamplingSampleTypeValues[int(sample_type)])
           .SetParam("num_args", num_args)
           .SetParam("num_filter", num_filter)
           .SetParam("multi_input_mode", UpSamplingMultiInputModeValues[int(multi_input_mode)])
           .SetParam("workspace", workspace)
(data)
           .CreateSymbol();
}

inline Symbol Concat(const std::vector<Symbol>& data,
                     int num_args,
                     int dim = 1) {
  return Operator("Concat")
           .SetParam("num_args", num_args)
           .SetParam("dim", dim)
(data)
           .CreateSymbol();
}

inline Symbol LayerNorm(Symbol data,
                        Symbol gamma,
                        Symbol beta,
                        int axis = -1,
                        mx_float eps = 9.99999975e-06,
                        bool output_mean_var = false) {
  return Operator("LayerNorm")
           .SetParam("axis", axis)
           .SetParam("eps", eps)
           .SetParam("output_mean_var", output_mean_var)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .SetInput("beta", beta)
           .CreateSymbol();
}

inline Symbol LRN(Symbol data,
                  uint32_t nsize,
                  mx_float alpha = 9.99999975e-05,
                  mx_float beta = 0.75,
                  mx_float knorm = 2) {
  return Operator("LRN")
           .SetParam("nsize", nsize)
           .SetParam("alpha", alpha)
           .SetParam("beta", beta)
           .SetParam("knorm", knorm)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol Dropout(Symbol data,
                      mx_float p = 0.5,
                      DropoutMode mode = DropoutMode::kTraining,
                      Shape axes = Shape(),
                      dmlc::optional<bool> cudnn_off = dmlc::optional<bool>(0)) {
  static const char *DropoutModeValues[] = {
    "always",
    "training"
  };
  return Operator("Dropout")
           .SetParam("p", p)
           .SetParam("mode", DropoutModeValues[int(mode)])
           .SetParam("axes", axes)
           .SetParam("cudnn_off", cudnn_off)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol SoftmaxActivation(Symbol data,
                                SoftmaxActivationMode mode = SoftmaxActivationMode::kInstance) {
  static const char *SoftmaxActivationModeValues[] = {
    "channel",
    "instance"
  };
  return Operator("SoftmaxActivation")
           .SetParam("mode", SoftmaxActivationModeValues[int(mode)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol moments(Symbol data,
                      dmlc::optional<Shape> axes = dmlc::optional<Shape>(),
                      bool keepdims = false) {
  return Operator("moments")
           .SetParam("axes", axes)
           .SetParam("keepdims", keepdims)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol LeakyReLU(Symbol data,
                        Symbol gamma,
                        LeakyReLUActType act_type = LeakyReLUActType::kLeaky,
                        mx_float slope = 0.25,
                        mx_float lower_bound = 0.125,
                        mx_float upper_bound = 0.333999991) {
  static const char *LeakyReLUActTypeValues[] = {
    "elu",
    "gelu",
    "leaky",
    "prelu",
    "rrelu",
    "selu"
  };
  return Operator("LeakyReLU")
           .SetParam("act_type", LeakyReLUActTypeValues[int(act_type)])
           .SetParam("slope", slope)
           .SetParam("lower_bound", lower_bound)
           .SetParam("upper_bound", upper_bound)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .CreateSymbol();
}

inline Symbol RNN(Symbol data,
                  Symbol parameters,
                  Symbol state,
                  Symbol state_cell,
                  Symbol sequence_length,
                  uint32_t state_size,
                  uint32_t num_layers,
                  RNNMode mode,
                  bool bidirectional = false,
                  mx_float p = 0,
                  bool state_outputs = false,
                  dmlc::optional<int> projection_size = dmlc::optional<int>(),
                  dmlc::optional<double> lstm_state_clip_min = dmlc::optional<double>(),
                  dmlc::optional<double> lstm_state_clip_max = dmlc::optional<double>(),
                  bool lstm_state_clip_nan = false,
                  bool use_sequence_length = false) {
  static const char *RNNModeValues[] = {
    "gru",
    "lstm",
    "rnn_relu",
    "rnn_tanh"
  };
  return Operator("RNN")
           .SetParam("state_size", state_size)
           .SetParam("num_layers", num_layers)
           .SetParam("mode", RNNModeValues[int(mode)])
           .SetParam("bidirectional", bidirectional)
           .SetParam("p", p)
           .SetParam("state_outputs", state_outputs)
           .SetParam("projection_size", projection_size)
           .SetParam("lstm_state_clip_min", lstm_state_clip_min)
           .SetParam("lstm_state_clip_max", lstm_state_clip_max)
           .SetParam("lstm_state_clip_nan", lstm_state_clip_nan)
           .SetParam("use_sequence_length", use_sequence_length)
           .SetInput("data", data)
           .SetInput("parameters", parameters)
           .SetInput("state", state)
           .SetInput("state_cell", state_cell)
           .SetInput("sequence_length", sequence_length)
           .CreateSymbol();
}

inline Symbol SoftmaxOutput(Symbol data,
                            Symbol label,
                            mx_float grad_scale = 1,
                            mx_float ignore_label = -1,
                            bool multi_output = false,
                            bool use_ignore = false,
                            bool preserve_shape = false,
                            SoftmaxOutputNormalization normalization = SoftmaxOutputNormalization::kNull,
                            bool out_grad = false,
                            mx_float smooth_alpha = 0) {
  static const char *SoftmaxOutputNormalizationValues[] = {
    "batch",
    "null",
    "valid"
  };
  return Operator("SoftmaxOutput")
           .SetParam("grad_scale", grad_scale)
           .SetParam("ignore_label", ignore_label)
           .SetParam("multi_output", multi_output)
           .SetParam("use_ignore", use_ignore)
           .SetParam("preserve_shape", preserve_shape)
           .SetParam("normalization", SoftmaxOutputNormalizationValues[int(normalization)])
           .SetParam("out_grad", out_grad)
           .SetParam("smooth_alpha", smooth_alpha)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol();
}

inline Symbol SwapAxis(Symbol data,
                       uint32_t dim1 = 0,
                       uint32_t dim2 = 0) {
  return Operator("SwapAxis")
           .SetParam("dim1", dim1)
           .SetParam("dim2", dim2)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol BatchNorm_v1(Symbol data,
                           Symbol gamma,
                           Symbol beta,
                           mx_float eps = 0.00100000005,
                           mx_float momentum = 0.899999976,
                           bool fix_gamma = true,
                           bool use_global_stats = false,
                           bool output_mean_var = false) {
  return Operator("BatchNorm_v1")
           .SetParam("eps", eps)
           .SetParam("momentum", momentum)
           .SetParam("fix_gamma", fix_gamma)
           .SetParam("use_global_stats", use_global_stats)
           .SetParam("output_mean_var", output_mean_var)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .SetInput("beta", beta)
           .CreateSymbol();
}

inline Symbol softmax_cross_entropy(Symbol data,
                                    Symbol label) {
  return Operator("softmax_cross_entropy")
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol();
}

inline Symbol LinearRegressionOutput(Symbol data,
                                     Symbol label,
                                     mx_float grad_scale = 1) {
  return Operator("LinearRegressionOutput")
           .SetParam("grad_scale", grad_scale)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol();
}

inline Symbol MAERegressionOutput(Symbol data,
                                  Symbol label,
                                  mx_float grad_scale = 1) {
  return Operator("MAERegressionOutput")
           .SetParam("grad_scale", grad_scale)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol();
}

inline Symbol LogisticRegressionOutput(Symbol data,
                                       Symbol label,
                                       mx_float grad_scale = 1) {
  return Operator("LogisticRegressionOutput")
           .SetParam("grad_scale", grad_scale)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol();
}

inline Symbol IdentityAttachKLSparseReg(Symbol data,
                                        mx_float sparseness_target = 0.100000001,
                                        mx_float penalty = 0.00100000005,
                                        mx_float momentum = 0.899999976) {
  return Operator("IdentityAttachKLSparseReg")
           .SetParam("sparseness_target", sparseness_target)
           .SetParam("penalty", penalty)
           .SetParam("momentum", momentum)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol signsgd_update(Symbol weight,
                             Symbol grad,
                             mx_float lr,
                             mx_float wd = 0,
                             mx_float rescale_grad = 1,
                             mx_float clip_gradient = -1) {
  return Operator("signsgd_update")
           .SetParam("lr", lr)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .CreateSymbol();
}

inline Symbol signum_update(Symbol weight,
                            Symbol grad,
                            Symbol mom,
                            mx_float lr,
                            mx_float momentum = 0,
                            mx_float wd = 0,
                            mx_float rescale_grad = 1,
                            mx_float clip_gradient = -1,
                            mx_float wd_lh = 0) {
  return Operator("signum_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("wd_lh", wd_lh)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .CreateSymbol();
}

inline Symbol multi_sgd_update(const std::vector<Symbol>& data,
                               nnvm::Tuple<mx_float> lrs,
                               nnvm::Tuple<mx_float> wds,
                               mx_float rescale_grad = 1,
                               mx_float clip_gradient = -1,
                               int num_weights = 1) {
  return Operator("multi_sgd_update")
           .SetParam("lrs", lrs)
           .SetParam("wds", wds)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("num_weights", num_weights)
(data)
           .CreateSymbol();
}

inline Symbol multi_sgd_mom_update(const std::vector<Symbol>& data,
                                   nnvm::Tuple<mx_float> lrs,
                                   nnvm::Tuple<mx_float> wds,
                                   mx_float momentum = 0,
                                   mx_float rescale_grad = 1,
                                   mx_float clip_gradient = -1,
                                   int num_weights = 1) {
  return Operator("multi_sgd_mom_update")
           .SetParam("lrs", lrs)
           .SetParam("wds", wds)
           .SetParam("momentum", momentum)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("num_weights", num_weights)
(data)
           .CreateSymbol();
}

inline Symbol multi_mp_sgd_update(const std::vector<Symbol>& data,
                                  nnvm::Tuple<mx_float> lrs,
                                  nnvm::Tuple<mx_float> wds,
                                  mx_float rescale_grad = 1,
                                  mx_float clip_gradient = -1,
                                  int num_weights = 1) {
  return Operator("multi_mp_sgd_update")
           .SetParam("lrs", lrs)
           .SetParam("wds", wds)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("num_weights", num_weights)
(data)
           .CreateSymbol();
}

inline Symbol multi_mp_sgd_mom_update(const std::vector<Symbol>& data,
                                      nnvm::Tuple<mx_float> lrs,
                                      nnvm::Tuple<mx_float> wds,
                                      mx_float momentum = 0,
                                      mx_float rescale_grad = 1,
                                      mx_float clip_gradient = -1,
                                      int num_weights = 1) {
  return Operator("multi_mp_sgd_mom_update")
           .SetParam("lrs", lrs)
           .SetParam("wds", wds)
           .SetParam("momentum", momentum)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("num_weights", num_weights)
(data)
           .CreateSymbol();
}

inline Symbol sgd_update(Symbol weight,
                         Symbol grad,
                         mx_float lr,
                         mx_float wd = 0,
                         mx_float rescale_grad = 1,
                         mx_float clip_gradient = -1,
                         bool lazy_update = true) {
  return Operator("sgd_update")
           .SetParam("lr", lr)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .CreateSymbol();
}

inline Symbol sgd_mom_update(Symbol weight,
                             Symbol grad,
                             Symbol mom,
                             mx_float lr,
                             mx_float momentum = 0,
                             mx_float wd = 0,
                             mx_float rescale_grad = 1,
                             mx_float clip_gradient = -1,
                             bool lazy_update = true) {
  return Operator("sgd_mom_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .CreateSymbol();
}

inline Symbol mp_sgd_update(Symbol weight,
                            Symbol grad,
                            Symbol weight32,
                            mx_float lr,
                            mx_float wd = 0,
                            mx_float rescale_grad = 1,
                            mx_float clip_gradient = -1,
                            bool lazy_update = true) {
  return Operator("mp_sgd_update")
           .SetParam("lr", lr)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("weight32", weight32)
           .CreateSymbol();
}

inline Symbol mp_sgd_mom_update(Symbol weight,
                                Symbol grad,
                                Symbol mom,
                                Symbol weight32,
                                mx_float lr,
                                mx_float momentum = 0,
                                mx_float wd = 0,
                                mx_float rescale_grad = 1,
                                mx_float clip_gradient = -1,
                                bool lazy_update = true) {
  return Operator("mp_sgd_mom_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .SetInput("weight32", weight32)
           .CreateSymbol();
}

inline Symbol ftml_update(Symbol weight,
                          Symbol grad,
                          Symbol d,
                          Symbol v,
                          Symbol z,
                          mx_float lr,
                          int t,
                          mx_float beta1 = 0.600000024,
                          mx_float beta2 = 0.999000013,
                          double epsilon = 9.9999999392252903e-09,
                          mx_float wd = 0,
                          mx_float rescale_grad = 1,
                          mx_float clip_grad = -1) {
  return Operator("ftml_update")
           .SetParam("lr", lr)
           .SetParam("t", t)
           .SetParam("beta1", beta1)
           .SetParam("beta2", beta2)
           .SetParam("epsilon", epsilon)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_grad", clip_grad)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("d", d)
           .SetInput("v", v)
           .SetInput("z", z)
           .CreateSymbol();
}

inline Symbol adam_update(Symbol weight,
                          Symbol grad,
                          Symbol mean,
                          Symbol var,
                          mx_float lr,
                          mx_float beta1 = 0.899999976,
                          mx_float beta2 = 0.999000013,
                          mx_float epsilon = 9.99999994e-09,
                          mx_float wd = 0,
                          mx_float rescale_grad = 1,
                          mx_float clip_gradient = -1,
                          bool lazy_update = true) {
  return Operator("adam_update")
           .SetParam("lr", lr)
           .SetParam("beta1", beta1)
           .SetParam("beta2", beta2)
           .SetParam("epsilon", epsilon)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("lazy_update", lazy_update)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mean", mean)
           .SetInput("var", var)
           .CreateSymbol();
}

inline Symbol nag_mom_update(Symbol weight,
                             Symbol grad,
                             Symbol mom,
                             mx_float lr,
                             mx_float momentum = 0,
                             mx_float wd = 0,
                             mx_float rescale_grad = 1,
                             mx_float clip_gradient = -1) {
  return Operator("nag_mom_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .CreateSymbol();
}

inline Symbol mp_nag_mom_update(Symbol weight,
                                Symbol grad,
                                Symbol mom,
                                Symbol weight32,
                                mx_float lr,
                                mx_float momentum = 0,
                                mx_float wd = 0,
                                mx_float rescale_grad = 1,
                                mx_float clip_gradient = -1) {
  return Operator("mp_nag_mom_update")
           .SetParam("lr", lr)
           .SetParam("momentum", momentum)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("mom", mom)
           .SetInput("weight32", weight32)
           .CreateSymbol();
}

inline Symbol rmsprop_update(Symbol weight,
                             Symbol grad,
                             Symbol n,
                             mx_float lr,
                             mx_float gamma1 = 0.949999988,
                             mx_float epsilon = 9.99999994e-09,
                             mx_float wd = 0,
                             mx_float rescale_grad = 1,
                             mx_float clip_gradient = -1,
                             mx_float clip_weights = -1) {
  return Operator("rmsprop_update")
           .SetParam("lr", lr)
           .SetParam("gamma1", gamma1)
           .SetParam("epsilon", epsilon)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("clip_weights", clip_weights)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("n", n)
           .CreateSymbol();
}

inline Symbol rmspropalex_update(Symbol weight,
                                 Symbol grad,
                                 Symbol n,
                                 Symbol g,
                                 Symbol delta,
                                 mx_float lr,
                                 mx_float gamma1 = 0.949999988,
                                 mx_float gamma2 = 0.899999976,
                                 mx_float epsilon = 9.99999994e-09,
                                 mx_float wd = 0,
                                 mx_float rescale_grad = 1,
                                 mx_float clip_gradient = -1,
                                 mx_float clip_weights = -1) {
  return Operator("rmspropalex_update")
           .SetParam("lr", lr)
           .SetParam("gamma1", gamma1)
           .SetParam("gamma2", gamma2)
           .SetParam("epsilon", epsilon)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetParam("clip_weights", clip_weights)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("n", n)
           .SetInput("g", g)
           .SetInput("delta", delta)
           .CreateSymbol();
}

inline Symbol ftrl_update(Symbol weight,
                          Symbol grad,
                          Symbol z,
                          Symbol n,
                          mx_float lr,
                          mx_float lamda1 = 0.00999999978,
                          mx_float beta = 1,
                          mx_float wd = 0,
                          mx_float rescale_grad = 1,
                          mx_float clip_gradient = -1) {
  return Operator("ftrl_update")
           .SetParam("lr", lr)
           .SetParam("lamda1", lamda1)
           .SetParam("beta", beta)
           .SetParam("wd", wd)
           .SetParam("rescale_grad", rescale_grad)
           .SetParam("clip_gradient", clip_gradient)
           .SetInput("weight", weight)
           .SetInput("grad", grad)
           .SetInput("z", z)
           .SetInput("n", n)
           .CreateSymbol();
}

inline Symbol SliceChannel(Symbol data,
                           int num_outputs,
                           int axis = 1,
                           bool squeeze_axis = false) {
  return Operator("SliceChannel")
           .SetParam("num_outputs", num_outputs)
           .SetParam("axis", axis)
           .SetParam("squeeze_axis", squeeze_axis)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol Pad(Symbol data,
                  PadMode mode,
                  Shape pad_width,
                  double constant_value = 0) {
  static const char *PadModeValues[] = {
    "constant",
    "edge",
    "reflect"
  };
  return Operator("Pad")
           .SetParam("mode", PadModeValues[int(mode)])
           .SetParam("pad_width", pad_width)
           .SetParam("constant_value", constant_value)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol InstanceNorm(Symbol data,
                           Symbol gamma,
                           Symbol beta,
                           mx_float eps = 0.00100000005) {
  return Operator("InstanceNorm")
           .SetParam("eps", eps)
           .SetInput("data", data)
           .SetInput("gamma", gamma)
           .SetInput("beta", beta)
           .CreateSymbol();
}

inline Symbol GridGenerator(Symbol data,
                            GridGeneratorTransformType transform_type,
                            Shape target_shape = Shape(0,0)) {
  static const char *GridGeneratorTransformTypeValues[] = {
    "affine",
    "warp"
  };
  return Operator("GridGenerator")
           .SetParam("transform_type", GridGeneratorTransformTypeValues[int(transform_type)])
           .SetParam("target_shape", target_shape)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol Pooling_v1(Symbol data,
                         Shape kernel = Shape(),
                         Pooling_v1PoolType pool_type = Pooling_v1PoolType::kMax,
                         bool global_pool = false,
                         Pooling_v1PoolingConvention pooling_convention = Pooling_v1PoolingConvention::kValid,
                         Shape stride = Shape(),
                         Shape pad = Shape()) {
  static const char *Pooling_v1PoolTypeValues[] = {
    "avg",
    "max",
    "sum"
  };
  static const char *Pooling_v1PoolingConventionValues[] = {
    "full",
    "valid"
  };
  return Operator("Pooling_v1")
           .SetParam("kernel", kernel)
           .SetParam("pool_type", Pooling_v1PoolTypeValues[int(pool_type)])
           .SetParam("global_pool", global_pool)
           .SetParam("pooling_convention", Pooling_v1PoolingConventionValues[int(pooling_convention)])
           .SetParam("stride", stride)
           .SetParam("pad", pad)
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol Convolution_v1(Symbol data,
                             Symbol weight,
                             Symbol bias,
                             Shape kernel,
                             uint32_t num_filter,
                             Shape stride = Shape(),
                             Shape dilate = Shape(),
                             Shape pad = Shape(),
                             uint32_t num_group = 1,
                             uint64_t workspace = 1024,
                             bool no_bias = false,
                             Convolution_v1CudnnTune cudnn_tune = Convolution_v1CudnnTune::kNone,
                             bool cudnn_off = false,
                             Convolution_v1Layout layout = Convolution_v1Layout::kNone) {
  static const char *Convolution_v1CudnnTuneValues[] = {
    "None",
    "fastest",
    "limited_workspace",
    "off"
  };
  static const char *Convolution_v1LayoutValues[] = {
    "None",
    "NCDHW",
    "NCHW",
    "NDHWC",
    "NHWC"
  };
  return Operator("Convolution_v1")
           .SetParam("kernel", kernel)
           .SetParam("num_filter", num_filter)
           .SetParam("stride", stride)
           .SetParam("dilate", dilate)
           .SetParam("pad", pad)
           .SetParam("num_group", num_group)
           .SetParam("workspace", workspace)
           .SetParam("no_bias", no_bias)
           .SetParam("cudnn_tune", Convolution_v1CudnnTuneValues[int(cudnn_tune)])
           .SetParam("cudnn_off", cudnn_off)
           .SetParam("layout", Convolution_v1LayoutValues[int(layout)])
           .SetInput("data", data)
           .SetInput("weight", weight)
           .SetInput("bias", bias)
           .CreateSymbol();
}

inline Symbol Crop(const std::vector<Symbol>& data,
                   int num_args,
                   Shape offset = Shape(0,0),
                   Shape h_w = Shape(0,0),
                   bool center_crop = false) {
  return Operator("Crop")
           .SetParam("num_args", num_args)
           .SetParam("offset", offset)
           .SetParam("h_w", h_w)
           .SetParam("center_crop", center_crop)
(data)
           .CreateSymbol();
}

inline Symbol SequenceReverse(Symbol data,
                              Symbol sequence_length,
                              bool use_sequence_length = false,
                              int axis = 0) {
  return Operator("SequenceReverse")
           .SetParam("use_sequence_length", use_sequence_length)
           .SetParam("axis", axis)
           .SetInput("data", data)
           .SetInput("sequence_length", sequence_length)
           .CreateSymbol();
}

inline Symbol SpatialTransformer(Symbol data,
                                 Symbol loc,
                                 SpatialTransformerTransformType transform_type,
                                 SpatialTransformerSamplerType sampler_type,
                                 Shape target_shape = Shape(0,0),
                                 dmlc::optional<bool> cudnn_off = dmlc::optional<bool>()) {
  static const char *SpatialTransformerTransformTypeValues[] = {
    "affine"
  };
  static const char *SpatialTransformerSamplerTypeValues[] = {
    "bilinear"
  };
  return Operator("SpatialTransformer")
           .SetParam("transform_type", SpatialTransformerTransformTypeValues[int(transform_type)])
           .SetParam("sampler_type", SpatialTransformerSamplerTypeValues[int(sampler_type)])
           .SetParam("target_shape", target_shape)
           .SetParam("cudnn_off", cudnn_off)
           .SetInput("data", data)
           .SetInput("loc", loc)
           .CreateSymbol();
}

inline Symbol BilinearSampler(Symbol data,
                              Symbol grid,
                              dmlc::optional<bool> cudnn_off = dmlc::optional<bool>()) {
  return Operator("BilinearSampler")
           .SetParam("cudnn_off", cudnn_off)
           .SetInput("data", data)
           .SetInput("grid", grid)
           .CreateSymbol();
}

inline Symbol ROIPooling(Symbol data,
                         Symbol rois,
                         Shape pooled_size,
                         mx_float spatial_scale) {
  return Operator("ROIPooling")
           .SetParam("pooled_size", pooled_size)
           .SetParam("spatial_scale", spatial_scale)
           .SetInput("data", data)
           .SetInput("rois", rois)
           .CreateSymbol();
}

inline Symbol SequenceLast(Symbol data,
                           Symbol sequence_length,
                           bool use_sequence_length = false,
                           int axis = 0) {
  return Operator("SequenceLast")
           .SetParam("use_sequence_length", use_sequence_length)
           .SetParam("axis", axis)
           .SetInput("data", data)
           .SetInput("sequence_length", sequence_length)
           .CreateSymbol();
}

inline Symbol L2Normalization(Symbol data,
                              mx_float eps = 1.00000001e-10,
                              L2NormalizationMode mode = L2NormalizationMode::kInstance) {
  static const char *L2NormalizationModeValues[] = {
    "channel",
    "instance",
    "spatial"
  };
  return Operator("L2Normalization")
           .SetParam("eps", eps)
           .SetParam("mode", L2NormalizationModeValues[int(mode)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol MakeLoss(Symbol data,
                       mx_float grad_scale = 1,
                       mx_float valid_thresh = 0,
                       MakeLossNormalization normalization = MakeLossNormalization::kNull) {
  static const char *MakeLossNormalizationValues[] = {
    "batch",
    "null",
    "valid"
  };
  return Operator("MakeLoss")
           .SetParam("grad_scale", grad_scale)
           .SetParam("valid_thresh", valid_thresh)
           .SetParam("normalization", MakeLossNormalizationValues[int(normalization)])
           .SetInput("data", data)
           .CreateSymbol();
}

inline Symbol SVMOutput(Symbol data,
                        Symbol label,
                        mx_float margin = 1,
                        mx_float regularization_coefficient = 1,
                        bool use_linear = false) {
  return Operator("SVMOutput")
           .SetParam("margin", margin)
           .SetParam("regularization_coefficient", regularization_coefficient)
           .SetParam("use_linear", use_linear)
           .SetInput("data", data)
           .SetInput("label", label)
           .CreateSymbol();
}

inline Symbol Correlation(Symbol data1,
                          Symbol data2,
                          uint32_t kernel_size = 1,
                          uint32_t max_displacement = 1,
                          uint32_t stride1 = 1,
                          uint32_t stride2 = 1,
                          uint32_t pad_size = 0,
                          bool is_multiply = true) {
  return Operator("Correlation")
           .SetParam("kernel_size", kernel_size)
           .SetParam("max_displacement", max_displacement)
           .SetParam("stride1", stride1)
           .SetParam("stride2", stride2)
           .SetParam("pad_size", pad_size)
           .SetParam("is_multiply", is_multiply)
           .SetInput("data1", data1)
           .SetInput("data2", data2)
           .CreateSymbol();
}

inline Symbol SequenceMask(Symbol data,
                           Symbol sequence_length,
                           bool use_sequence_length = false,
                           mx_float value = 0,
                           int axis = 0) {
  return Operator("SequenceMask")
           .SetParam("use_sequence_length", use_sequence_length)
           .SetParam("value", value)
           .SetParam("axis", axis)
           .SetInput("data", data)
           .SetInput("sequence_length", sequence_length)
           .CreateSymbol();
}

inline Symbol fill_element_0index(Symbol lhs,
                                  Symbol mhs,
                                  Symbol rhs) {
  return Operator("fill_element_0index")
           .SetInput("lhs", lhs)
           .SetInput("mhs", mhs)
           .SetInput("rhs", rhs)
           .CreateSymbol();
}

} //namespace cpp
} //namespace mxnet
#endif  // MXNET_CPP_OP_H_