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 /*
  * SPDX-License-Identifier: Apache-2.0
  */
 
 #pragma once
 
 #include <climits>
 #include <cstring>
 #include <functional>
 #include <initializer_list>
 #include <iostream>
 #include <limits>
 #include <map>
 #include <memory>
 #include <ostream>
 #include <set>
 #include <string>
 #include <string_view>
 #include <tuple>
 #include <unordered_map>
 #include <unordered_set>
 #include <utility>
 #include <vector>
 
 #include "onnx/common/common.h"
 #include "onnx/common/constants.h"
 #include "onnx/defs/shape_inference.h"
 
 namespace ONNX_NAMESPACE {
 
 struct FunctionBodyBuildContext {
   virtual const AttributeProto* getAttribute(const std::string& name) const = 0;
   virtual bool hasInput(int inputIndex) const = 0;
   virtual bool hasOutput(int inputIndex) const = 0;
   // getInputType(i) should return null for missing optional inputs, or if
   // type-inference could not infer the input-type (erroneous model).
   virtual const TypeProto* getInputType(int inputIndex) const = 0;
   virtual ~FunctionBodyBuildContext() {}
 };
 
 struct FunctionBodyBuildContextImpl : public FunctionBodyBuildContext {
   // Input_types: use a default TypeProto for missing types. We use a different convention
   // here (from FunctionBodyBuildContext) to simplify python interoperability.
   // The default value for input_types is included only for backward compatibility.
   // It can be used for functions that do not depend on the type-context, but
   // will not be sufficient for functions that do use the type-context.
   FunctionBodyBuildContextImpl(const NodeProto& node_proto, const std::vector<TypeProto>& input_types = {})
       : node_proto_(node_proto), input_types_(input_types) {
     for (auto& attr : node_proto.attribute()) {
       attributesByName_[attr.name()] = &attr;
     }
   }
 
   const AttributeProto* getAttribute(const std::string& name) const override {
     auto iter = attributesByName_.find(name);
     if (iter == attributesByName_.end()) {
       return nullptr;
     } else {
       return iter->second;
     }
   }
 
   bool hasInput(int inputIndex) const override {
     if (inputIndex >= node_proto_.input_size())
       return false;
     return node_proto_.input(inputIndex) != "";
   }
 
   bool hasOutput(int inputIndex) const override {
     if (inputIndex >= node_proto_.output_size())
       return false;
     return node_proto_.output(inputIndex) != "";
   }
 
   const TypeProto* getInputType(int inputIndex) const override {
     if (inputIndex < 0)
       return nullptr;
     size_t j = static_cast<size_t>(inputIndex);
     if (j >= input_types_.size())
       return nullptr;
     // Convert default value (no variant set) into null.
     if (input_types_[j].value_case() == TypeProto::ValueCase::VALUE_NOT_SET)
       return nullptr;
     return &input_types_[j];
   }
 
   std::unordered_map<std::string, const AttributeProto*> attributesByName_;
 
   NodeProto node_proto_;
   std::vector<TypeProto> input_types_;
 };
 
 using FunctionBodyQueryFunction = std::function<bool(FunctionBodyBuildContext&)>;
 
 class OpSchema;
 using ContextDependentFunctionBodyBuilder =
     std::function<bool(const FunctionBodyBuildContext&, const OpSchema&, FunctionProto&)>;
 
 class SchemaError final : public std::runtime_error {
  public:
   using std::runtime_error::runtime_error;
 
   SchemaError(const std::string& message) : std::runtime_error(message) {}
 
   const char* what() const noexcept override {
     if (!expanded_message_.empty()) {
       return expanded_message_.c_str();
     }
     return std::runtime_error::what();
   }
 
   void AppendContext(const std::string& context) {
     expanded_message_ = ONNX_NAMESPACE::MakeString(std::runtime_error::what(), "\n\n==> Context: ", context);
   }
 
  private:
   std::string expanded_message_;
 };
 
 #define fail_schema(...) ONNX_THROW_EX(ONNX_NAMESPACE::SchemaError(ONNX_NAMESPACE::MakeString(__VA_ARGS__)));
 
 using OperatorSetVersion = int;
 
 using DataTypeSet = std::unordered_set<DataType>;
 
 // Type constraint map. Key is type string. Value is data type set and
 // description.
 using TypeConstraintMap = std::unordered_map<std::string, std::pair<DataTypeSet, std::string>>;
 
 /**
  * @brief A class to record the schema of an op.
  *
  * OpSchema records the common interface of an op specified by its name.
  *
  * To register an OpSchema, one can use the macro ONNX_OPERATOR_SCHEMA(name) and
  * then append the various functions in the class. For example, for an op
  * that takes in two inputs, one output, and the first input and output
  * could be in-place, can be written as
  *
  *     ONNX_OPERATOR_SCHEMA(name)
  *         .NumInputs(2).NumOutputs(1).AllowConsumed({{0, 0}});
  *
  * To manufacture methods that may be used to register an OpSchema
  * non-statically, the following may be used:
  *
  *     ONNX_OPERATOR_SET_SCHEMA(name, version, OpSchema()
  *         .NumInputs(2).NumOutputs(1).AllowConsumed({{0, 0}}));
  */
 class OpSchema final {
  public:
   static constexpr int kUninitializedSinceVersion = -1;
   // Formal parameter options.
   enum FormalParameterOption : uint8_t {
     // The formal parameter is single and not optional.
     // Number of supplied actual parameters must be 1.
     Single = 0,
     // The formal parameter is single and optional.
     // Number of supplied actual parameters may be 0 or 1.
     Optional = 1,
     // The formal parameter is variadic.
     // Number of supplied actual parameters must be N or more, where
     // the minimum value N is indicated separately (default value 1).
     Variadic = 2,
   };
   enum DifferentiationCategory : uint8_t {
     // Whether this formal parameter is differentiable or not cannot
     // be statically determined. It also covers variadic formal
     // parameters which contain both of differentiable and
     // non-differentiable variables.
     Unknown = 0,
     // This formal parameter is differentiable. That is, this formal
     // parameter can be differentiable input of Gradient operator.
     Differentiable = 1,
     // This formal parameter is not differentiable. That is, this formal
     // parameter can not be differentiable input of Gradient operator.
     NonDifferentiable = 2
   };
 
   // Formal parameter represenation, including input/output name, typeStr,
   // description, and type constraints.
   class FormalParameter final {
    public:
     // Constructor.
     FormalParameter() = default;
 
     explicit FormalParameter(
         std::string name,
         DataTypeSet allowed_type_set,
         std::string type_str,
         const std::string& description,
         FormalParameterOption param_option = Single,
         bool is_homogeneous = true,
         int min_arity = 1,
         DifferentiationCategory differentiation_category = Unknown)
         : name_(std::move(name)),
           type_set_(std::move(allowed_type_set)),
           type_str_(std::move(type_str)),
 #ifndef __ONNX_NO_DOC_STRINGS
           description_(description),
 #endif
           param_option_(param_option),
           is_homogeneous_(is_homogeneous),
           min_arity_(min_arity),
           differentiation_category_(differentiation_category) {
 #ifdef __ONNX_NO_DOC_STRINGS
       ONNX_UNUSED_PARAMETER(description);
 #endif
     }
 
     explicit FormalParameter(
         std::string name,
         const std::string& description,
         std::string type_str,
         FormalParameterOption param_option = Single,
         bool is_homogeneous = true,
         int min_arity = 1,
         DifferentiationCategory differentiation_category = Unknown)
         : name_(std::move(name)),
           type_str_(std::move(type_str)),
 #ifndef __ONNX_NO_DOC_STRINGS
           description_(description),
 #endif
           param_option_(param_option),
           is_homogeneous_(is_homogeneous),
           min_arity_(min_arity),
           differentiation_category_(differentiation_category) {
 #ifdef __ONNX_NO_DOC_STRINGS
       ONNX_UNUSED_PARAMETER(description);
 #endif
     }
 
     // Get formal parameter name.
     const std::string& GetName() const;
 
     // Get allowed data types.
     const DataTypeSet& GetTypes() const;
 
     // Get formal parameter type string.
     const std::string& GetTypeStr() const;
 
     // Get formal parameter description.
     const std::string& GetDescription() const;
 
     // Get the parameter option, it could be Single, Optional or Variadic.
     FormalParameterOption GetOption() const;
 
     // Get whether a variadic parameter requires all to be of same type
     bool GetIsHomogeneous() const;
 
     // Get minimum arity. Applicable only in the Variadic case.
     int GetMinArity() const;
 
     // Get the differentiation property of this formal parameter.
     DifferentiationCategory GetDifferentiationCategory() const;
 
    private:
     friend class OpSchema;
 
     DataTypeSet& MutableTypes();
 
     // Formal parameter name.
     std::string name_;
 
     // A set of data types supported for <*this> formal parameter.
     // It should contain at least one element if this formal parameter is good.
     DataTypeSet type_set_;
 
     // The <parameter type> string specified when registring an op.
     // It could be a supported data type or a type constraint key, which
     // maps to a set of supported data types.
     std::string type_str_;
 
     // Formal parameter description.
     std::string description_;
 
     // Formal parameter option.
     FormalParameterOption param_option_;
 
     // For variadic parameters, a flag indicating if all parameters must be of
     // same type
     bool is_homogeneous_;
 
     // Minimum number of parameters expected. Applicable only for Variadic.
     int min_arity_;
 
     // True if this parameter can be an differentiable inputs of Gradient.
     // Otherwise, using this parameter as an differentiable inputs of Gradient
     // is prohibited.
     DifferentiationCategory differentiation_category_;
   };
 
   enum class SupportType : uint8_t {
     COMMON, // Supported by all frameworks that support this IR.
     EXPERIMENTAL, // This OP is experimental and can be changed or removed in
                   // the future.
   };
 
   OpSchema() : OpSchema("unknown", "unknown", 0) {}
   OpSchema(std::string name, std::string file, int line)
       : name_(std::move(name)), file_(std::move(file)), line_(line), support_(SupportType::COMMON) {}
 
   /**
    * @brief Returns the file that the op schema is registered from.
    */
   const std::string& file() const {
     return file_;
   }
 
   /**
    * @brief Returns the line in file that the op schema is registered from.
    */
   int line() const {
     return line_;
   }
 
   /**
    * @brief Returns the support level of the op schema.
    */
   SupportType support_level() const {
     return support_;
   }
 
   /**
    * @brief Returns the docstring of the op schema.
    */
   const char* doc() const {
     return doc_.empty() ? nullptr : doc_.c_str();
   }
 
   // Check if input and output types fall into valid set and match each other
   void CheckInputOutputType(struct InferenceContext&) const;
 
   /**
    * @brief Verifies if a NodeProto matches the pattern specified in
    * the schema.
    */
   void Verify(const NodeProto& node) const;
 
   // Functions to set the property of the operator schemas.
   // Sets the number of inputs, either a fixed number or a min and a max.
 
   /**
    * The earliest operator set version which this operator was
    * present in.  If an operator has had no BC-breaking changes,
    * this is simply the first operator set the operator was a member
    * of; if it has had BC-breaking changes, then for the semantics
    * /as described/ in the OpSchema entry, this version describes
    * the operator set which introduced the BC-breaking change.
    *
    * For example, suppose op Foo was added in v3, and had a BC-breaking
    * change in v6.  Then there will be an op schema entry for Foo with
    * SinceVersion(3), and another, updated op schema entry for Foo
    * with SinceVersion(6).
    */
   OpSchema& SinceVersion(OperatorSetVersion n); // aka int
 
   /**
    * Marks this op as deprecated as of it's since_version. This will cause the
    * Schema() lookup functions to return nullptr when the version is in the
    * deprecated range.
    */
   OpSchema& Deprecate();
 
   bool Deprecated() const {
     return deprecated_;
   }
 
   /**
    * @brief Input could be one of the values specified in allowed_input_nums.
    */
   OpSchema& NumInputs(std::set<int> allowed_input_nums);
 
   /**
    * @brief Output could be one of the values specified in allowed_output_nums.
    */
   OpSchema& NumOutputs(std::set<int> allowed_output_nums);
 
   // Shape Inference
   //
   // Note that signatures are defined to allow for forward-declaring
   // any structs used from ir.h
   OpSchema& TypeAndShapeInferenceFunction(InferenceFunction inferenceFunction);
   InferenceFunction GetTypeAndShapeInferenceFunction() const {
     return tensor_inference_function_ ? tensor_inference_function_ : dummyInferenceFunction;
   }
 
   OpSchema& PartialDataPropagationFunction(DataPropagationFunction dataProgationFunction);
   DataPropagationFunction GetDataPropagationFunction() const {
     return data_propagation_function_ ? data_propagation_function_ : dummyDataPropagationFunction;
   }
 
   // Set the support level for the op schema.
   OpSchema& SetSupportLevel(SupportType supportType);
 
   // Functions to do documentation for the operator schema.
   // This may be disabled to save memory.
   OpSchema& SetDoc(const char* doc) {
 #ifndef __ONNX_NO_DOC_STRINGS
     SetDoc(std::string(doc));
 #else
     ONNX_UNUSED_PARAMETER(doc);
 #endif
 
     return *this;
   }
 
   OpSchema& SetDoc(const std::string& doc) {
 #ifndef __ONNX_NO_DOC_STRINGS
     doc_ = doc;
 #else
     ONNX_UNUSED_PARAMETER(doc);
 #endif
     return *this;
   }
 
   // Functions to specify name for the operator schema.
   OpSchema& SetName(const char* name);
   OpSchema& SetName(std::string name);
 
   // Functions to specify code location for the operator schema.
   OpSchema& SetLocation(const char* file, int line);
   OpSchema& SetLocation(std::string file, int line);
 
   // Functions to specify domain for the operator schema.
   // Default domain value (ONNX_DOMAIN) means it's ONNX domain.
   OpSchema& SetDomain(const char* domain);
   OpSchema& SetDomain(std::string domain);
 
   struct Attribute final {
     Attribute(std::string name_, std::string description_, AttributeProto::AttributeType type_, bool required_)
         : name(std::move(name_)),
           description(std::move(description_)),
           type(type_),
           required(required_),
           default_value() {}
 
     Attribute(std::string name_, std::string description_, AttributeProto default_value_)
         : name(std::move(name_)),
           description(std::move(description_)),
           type(default_value_.type()),
           required(false),
           default_value(std::move(default_value_)) {}
 
     const std::string name;
     const std::string description;
     AttributeProto::AttributeType type;
     bool required;
     AttributeProto default_value;
   };
 
   OpSchema& Attr(Attribute attr);
 
 // Register "optional" attribute with default value.
 #define ATTR_SETTER_WITH_DEFAULT_VALUE(TypeName)                                                                    \
   OpSchema& Attr(                                                                                                   \
       std::string name, std::string description, AttributeProto::AttributeType type, const TypeName& defaultValue); \
   /* non-STL wrapper to reduce binary size */                                                                       \
   OpSchema& Attr(                                                                                                   \
       const char* name, const char* description, AttributeProto::AttributeType type, const TypeName& defaultValue); \
   OpSchema& Attr(                                                                                                   \
       std::string name,                                                                                             \
       std::string description,                                                                                      \
       AttributeProto::AttributeType type,                                                                           \
       const std::vector<TypeName>& defaultValue);
 
   ATTR_SETTER_WITH_DEFAULT_VALUE(int64_t)
   ATTR_SETTER_WITH_DEFAULT_VALUE(float)
   ATTR_SETTER_WITH_DEFAULT_VALUE(std::string)
   ATTR_SETTER_WITH_DEFAULT_VALUE(TensorProto)
   ATTR_SETTER_WITH_DEFAULT_VALUE(GraphProto)
   ATTR_SETTER_WITH_DEFAULT_VALUE(TypeProto)
 
   OpSchema& Attr(
       std::string name,
       std::string description,
       std::string conditionExplanation,
       AttributeProto::AttributeType attr_type);
 
   // Register "required" attribute without default value.
   OpSchema& Attr(std::string name, std::string description, AttributeProto::AttributeType type, bool required = true);
 
   // Non-STL wrapper to reduce binary size
   OpSchema& Attr(const char* name, const char* description, AttributeProto::AttributeType type, bool required = true);
 
   OpSchema& AllowUncheckedAttributes();
 
   // Type constraint.
   struct TypeConstraintParam final {
     TypeConstraintParam(
         std::string type_param_str_,
         std::vector<std::string> allowed_type_strs_,
         std::string description_)
         : type_param_str(std::move(type_param_str_)),
           allowed_type_strs(std::move(allowed_type_strs_)),
           description(std::move(description_)) {}
 
     // Type parameter string, for example, "T", "T1", etc.
     std::string type_param_str;
     // Allowed type strings for <*this> type parameter, for example,
     // "tensor(float)".
     std::vector<std::string> allowed_type_strs;
     // Type parameter description.
     std::string description;
   };
 
   // Grammar for type strings used in Input(), Output().
   // <type> ::= <data_type> |
   //            tensor(<data_type>) |
   //            seq(<type>) |
   //            map(<data_type>, <type>) |
   //            <type_parameter>
   // <data_type> :: = float | int32 | string | bool | uint8
   //                | int8 | uint16 | int16 | int64 | float16 | double
   // <type_parameter> ::= any type parameter string, say "T".
   //
   // NOTE: 1) <type_parameter> will always be together with a type constraints
   // specification.
   //       2) <type> ::= <data_type> means the data is scalar (zero dimension).
   //
   // Example:
   // ONNX_OPERATOR_SET_SCHEMA(Sum, 1, OpSchema()
   // .Input(0, "input_a", "the first input", "T")
   // .Input(1, "input_b", "the second input", "T")
   // .Output(0, "sum", "the sum of two numbers", "T")
   // .TypeConstraint("T", {"float", "double", "int32"}, "allowed data types for
   // sum."))
   //
   // Optional = true means that the input might have empty input value
   // (represented as "") in the graph even though the later inputs have values.
   // It's useful for complex situation when there are several independent
   // optional inputs.
   OpSchema& Input(int n, FormalParameter formal_parameter);
 
   OpSchema& Input(
       int n,
       std::string name,
       const std::string& description,
       std::string type_str,
       FormalParameterOption param_option = Single,
       bool is_homogeneous = true,
       int min_arity = 1,
       DifferentiationCategory differentiation_category = Unknown);
 
   // Non-STL wrapper to reduce binary size
   OpSchema& Input(
       int n,
       const char* name,
       const char* description,
       const char* type_str,
       FormalParameterOption param_option = Single,
       bool is_homogeneous = true,
       int min_arity = 1,
       DifferentiationCategory differentiation_category = Unknown);
 
   OpSchema& Output(int n, FormalParameter formal_parameter);
 
   OpSchema& Output(
       int n,
       std::string name,
       const std::string& description,
       std::string type_str,
       FormalParameterOption param_option = Single,
       bool is_homogeneous = true,
       int min_arity = 1,
       DifferentiationCategory differentiation_category = Unknown);
 
   // Non-STL wrapper to reduce binary size
   OpSchema& Output(
       int n,
       const char* name,
       const char* description,
       const char* type_str,
       FormalParameterOption param_option = Single,
       bool is_homogeneous = true,
       int min_arity = 1,
       DifferentiationCategory differentiation_category = Unknown);
 
   OpSchema& TypeConstraint(std::string type_str, std::vector<std::string> constraints, std::string description);
 
   // Non-STL wrapper to reduce binary size
   OpSchema&
   TypeConstraint(const char* type_str, std::initializer_list<const char*> constraints, const char* description);
 
   // Convenience members for types
 
   // All high-precision numeric types.
   static const std::vector<std::string>& numeric_types_for_math_reduction_ir10() {
     return numeric_types_for_math_reduction_ir9();
   }
 
   static const std::vector<std::string>& numeric_types_for_math_reduction_ir9() {
     static const std::vector<std::string> numeric_types_for_math_reduction_ir9 = {
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)",
         "tensor(bfloat16)",
         "tensor(float8e4m3fn)",
         "tensor(float8e4m3fnuz)",
         "tensor(float8e5m2)",
         "tensor(float8e5m2fnuz)"};
     return numeric_types_for_math_reduction_ir9;
   }
 
   static const std::vector<std::string>& numeric_types_for_math_reduction_ir4() {
     static const std::vector<std::string> numeric_types_for_math_reduction_ir4 = {
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)",
         "tensor(bfloat16)"};
     return numeric_types_for_math_reduction_ir4;
   }
 
   static const std::vector<std::string>& numeric_types_for_math_reduction() {
     static const std::vector<std::string> numeric_types_for_math_reduction = {
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)"};
     return numeric_types_for_math_reduction;
   }
 
   static const std::vector<std::string>& all_numeric_types_ir10() {
     static const std::vector<std::string> all_numeric_types_ir10 = {
         "tensor(uint8)",
         "tensor(uint16)",
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int8)",
         "tensor(int16)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)",
         "tensor(bfloat16)",
         "tensor(float8e4m3fn)",
         "tensor(float8e4m3fnuz)",
         "tensor(float8e5m2)",
         "tensor(float8e5m2fnuz)",
         "tensor(uint4)",
         "tensor(int4)"};
     return all_numeric_types_ir10;
   }
 
   static const std::vector<std::string>& all_numeric_types_ir9() {
     static const std::vector<std::string> all_numeric_types_ir9 = {
         "tensor(uint8)",
         "tensor(uint16)",
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int8)",
         "tensor(int16)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)",
         "tensor(bfloat16)",
         "tensor(float8e4m3fn)",
         "tensor(float8e4m3fnuz)",
         "tensor(float8e5m2)",
         "tensor(float8e5m2fnuz)"};
     return all_numeric_types_ir9;
   }
 
   static const std::vector<std::string>& all_numeric_types_ir4() {
     static const std::vector<std::string> all_numeric_types_ir4 = {
         "tensor(uint8)",
         "tensor(uint16)",
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int8)",
         "tensor(int16)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)",
         "tensor(bfloat16)"};
     return all_numeric_types_ir4;
   }
 
   static const std::vector<std::string>& all_numeric_types() {
     static const std::vector<std::string> all_numeric_types = {
         "tensor(uint8)",
         "tensor(uint16)",
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int8)",
         "tensor(int16)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)"};
     return all_numeric_types;
   }
 
   static const std::vector<std::string>& all_numeric_sequence_types() {
     static const std::vector<std::string> all_numeric_sequence_types = {
         "seq(tensor(uint8))",
         "seq(tensor(uint16))",
         "seq(tensor(uint32))",
         "seq(tensor(uint64))",
         "seq(tensor(int8))",
         "seq(tensor(int16))",
         "seq(tensor(int32))",
         "seq(tensor(int64))",
         "seq(tensor(float16))",
         "seq(tensor(float))",
         "seq(tensor(double))"};
     return all_numeric_sequence_types;
   }
 
   static const std::vector<std::string>& all_tensor_types() {
     static const std::vector<std::string> all_tensor_types = {
         "tensor(uint8)",
         "tensor(uint16)",
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int8)",
         "tensor(int16)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)",
         "tensor(string)",
         "tensor(bool)",
         "tensor(complex64)",
         "tensor(complex128)"};
     return all_tensor_types;
   }
 
   static const std::vector<std::string>& all_tensor_types_ir4() {
     static const std::vector<std::string> all_tensor_types_ir4 = {
         "tensor(uint8)",
         "tensor(uint16)",
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int8)",
         "tensor(int16)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(bfloat16)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)",
         "tensor(string)",
         "tensor(bool)",
         "tensor(complex64)",
         "tensor(complex128)"};
     return all_tensor_types_ir4;
   }
 
   static const std::vector<std::string>& all_non_complex_numeric_types_plus_bool_ir4() {
     static const std::vector<std::string> all_non_complex_numeric_types_plus_bool_ir4 = {
         "tensor(uint8)",
         "tensor(uint16)",
         "tensor(uint32)",
         "tensor(uint64)",
         "tensor(int8)",
         "tensor(int16)",
         "tensor(int32)",
         "tensor(int64)",
         "tensor(bfloat16)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)",
         "tensor(bool)"};
     return all_non_complex_numeric_types_plus_bool_ir4;
   }
 
   static const std::vector<std::string>& all_float_types_ir4() {
     static const std::vector<std::string> all_float_types_ir4 = {
         "tensor(bfloat16)", "tensor(float16)", "tensor(float)", "tensor(double)"};
     return all_float_types_ir4;
   }
 
   static const std::vector<std::string>& all_float_types_plus_Xint8_ir4() {
     static const std::vector<std::string> all_float_types_ir4 = {
         "tensor(bfloat16)", "tensor(float16)", "tensor(float)", "tensor(double)", "tensor(int8)", "tensor(uint8)"};
     return all_float_types_ir4;
   }
 
   static const std::vector<std::string>& all_float_types_ir9() {
     static const std::vector<std::string> all_float_types_ir9 = {
         "tensor(bfloat16)",
         "tensor(float16)",
         "tensor(float)",
         "tensor(double)",
         "tensor(float8e4m3fn)",
         "tensor(float8e4m3fnuz)",
         "tensor(float8e5m2)",
         "tensor(float8e5m2fnuz)"};
     return all_float_types_ir9;
   }
 
   static const std::vector<std::string>& all_float_types_ir10() {
     return all_float_types_ir9();
   }
 
   static const std::vector<std::string>& all_tensor_types_ir9() {
     static const std::vector<std::string> all_tensor_types_ir9 = {
         "tensor(uint8)",        "tensor(uint16)",         "tensor(uint32)",     "tensor(uint64)",
         "tensor(int8)",         "tensor(int16)",          "tensor(int32)",      "tensor(int64)",
         "tensor(bfloat16)",     "tensor(float16)",        "tensor(float)",      "tensor(double)",
         "tensor(string)",       "tensor(bool)",           "tensor(complex64)",  "tensor(complex128)",
         "tensor(float8e4m3fn)", "tensor(float8e4m3fnuz)", "tensor(float8e5m2)", "tensor(float8e5m2fnuz)"};
     return all_tensor_types_ir9;
   }
 
   static const std::vector<std::string>& all_tensor_types_ir10() {
     static const std::vector<std::string> all_tensor_types_ir10 = {
         "tensor(uint8)",      "tensor(uint16)",         "tensor(uint32)",
         "tensor(uint64)",     "tensor(int8)",           "tensor(int16)",
         "tensor(int32)",      "tensor(int64)",          "tensor(bfloat16)",
         "tensor(float16)",    "tensor(float)",          "tensor(double)",
         "tensor(string)",     "tensor(bool)",           "tensor(complex64)",
         "tensor(complex128)", "tensor(float8e4m3fn)",   "tensor(float8e4m3fnuz)",
         "tensor(float8e5m2)", "tensor(float8e5m2fnuz)", "tensor(uint4)",
         "tensor(int4)"};
     return all_tensor_types_ir10;
   }
 
   static const std::vector<std::string>& all_non_complex_tensor_types_ir10() {
     static const std::vector<std::string> all_non_complex_tensor_types_ir10 = {
         "tensor(uint8)",      "tensor(uint16)",         "tensor(uint32)",       "tensor(uint64)",
         "tensor(int8)",       "tensor(int16)",          "tensor(int32)",        "tensor(int64)",
         "tensor(bfloat16)",   "tensor(float16)",        "tensor(float)",        "tensor(double)",
         "tensor(string)",     "tensor(bool)",           "tensor(float8e4m3fn)", "tensor(float8e4m3fnuz)",
         "tensor(float8e5m2)", "tensor(float8e5m2fnuz)", "tensor(uint4)",        "tensor(int4)"};
     return all_non_complex_tensor_types_ir10;
   }
 
   static const std::vector<std::string>& all_tensor_sequence_types() {
     static const std::vector<std::string> all_tensor_sequence_types = {
         "seq(tensor(uint8))",
         "seq(tensor(uint16))",
         "seq(tensor(uint32))",
         "seq(tensor(uint64))",
         "seq(tensor(int8))",
         "seq(tensor(int16))",
         "seq(tensor(int32))",
         "seq(tensor(int64))",
         "seq(tensor(float16))",
         "seq(tensor(float))",
         "seq(tensor(double))",
         "seq(tensor(string))",
         "seq(tensor(bool))",
         "seq(tensor(complex64))",
         "seq(tensor(complex128))"};
     return all_tensor_sequence_types;
   }
 
   static const std::vector<std::string>& all_tensor_sequence_types_ir4() {
     static const std::vector<std::string> all_tensor_sequence_types_ir4 = {
         "seq(tensor(uint8))",
         "seq(tensor(uint16))",
         "seq(tensor(uint32))",
         "seq(tensor(uint64))",
         "seq(tensor(int8))",
         "seq(tensor(int16))",
         "seq(tensor(int32))",
         "seq(tensor(int64))",
         "seq(tensor(bfloat16))",
         "seq(tensor(float16))",
         "seq(tensor(float))",
         "seq(tensor(double))",
         "seq(tensor(string))",
         "seq(tensor(bool))",
         "seq(tensor(complex64))",
         "seq(tensor(complex128))"};
     return all_tensor_sequence_types_ir4;
   }
 
   static const std::vector<std::string>& all_tensor_sequence_types_ir9() {
     static const std::vector<std::string> all_tensor_sequence_types_ir9 = {
         "seq(tensor(uint8))",      "seq(tensor(uint16))",        "seq(tensor(uint32))",
         "seq(tensor(uint64))",     "seq(tensor(int8))",          "seq(tensor(int16))",
         "seq(tensor(int32))",      "seq(tensor(int64))",         "seq(tensor(bfloat16))",
         "seq(tensor(float16))",    "seq(tensor(float))",         "seq(tensor(double))",
         "seq(tensor(string))",     "seq(tensor(bool))",          "seq(tensor(complex64))",
         "seq(tensor(complex128))", "seq(tensor(float8e4m3fn))",  "seq(tensor(float8e4m3fnuz))",
         "seq(tensor(float8e5m2))", "seq(tensor(float8e5m2fnuz))"};
     return all_tensor_sequence_types_ir9;
   }
 
   static const std::vector<std::string>& all_tensor_sequence_types_ir10() {
     static const std::vector<std::string> all_tensor_sequence_types_ir10 = {
         "seq(tensor(uint8))",      "seq(tensor(uint16))",         "seq(tensor(uint32))",
         "seq(tensor(uint64))",     "seq(tensor(int8))",           "seq(tensor(int16))",
         "seq(tensor(int32))",      "seq(tensor(int64))",          "seq(tensor(bfloat16))",
         "seq(tensor(float16))",    "seq(tensor(float))",          "seq(tensor(double))",
         "seq(tensor(string))",     "seq(tensor(bool))",           "seq(tensor(complex64))",
         "seq(tensor(complex128))", "seq(tensor(float8e4m3fn))",   "seq(tensor(float8e4m3fnuz))",
         "seq(tensor(float8e5m2))", "seq(tensor(float8e5m2fnuz))", "seq(tensor(uint4))",
         "seq(tensor(int4))"};
     return all_tensor_sequence_types_ir10;
   }
 
   static const std::vector<std::string>& all_optional_types() {
     static const std::vector<std::string> all_optional_types = {
         "optional(seq(tensor(uint8)))",  "optional(seq(tensor(uint16)))",    "optional(seq(tensor(uint32)))",
         "optional(seq(tensor(uint64)))", "optional(seq(tensor(int8)))",      "optional(seq(tensor(int16)))",
         "optional(seq(tensor(int32)))",  "optional(seq(tensor(int64)))",     "optional(seq(tensor(float16)))",
         "optional(seq(tensor(float)))",  "optional(seq(tensor(double)))",    "optional(seq(tensor(string)))",
         "optional(seq(tensor(bool)))",   "optional(seq(tensor(complex64)))", "optional(seq(tensor(complex128)))",
         "optional(tensor(uint8))",       "optional(tensor(uint16))",         "optional(tensor(uint32))",
         "optional(tensor(uint64))",      "optional(tensor(int8))",           "optional(tensor(int16))",
         "optional(tensor(int32))",       "optional(tensor(int64))",          "optional(tensor(float16))",
         "optional(tensor(float))",       "optional(tensor(double))",         "optional(tensor(string))",
         "optional(tensor(bool))",        "optional(tensor(complex64))",      "optional(tensor(complex128))"};
     return all_optional_types;
   }
 
   static const std::vector<std::string>& all_optional_types_ir4() {
     static const std::vector<std::string> all_optional_types = {
         "optional(seq(tensor(uint8)))",      "optional(seq(tensor(uint16)))", "optional(seq(tensor(uint32)))",
         "optional(seq(tensor(uint64)))",     "optional(seq(tensor(int8)))",   "optional(seq(tensor(int16)))",
         "optional(seq(tensor(int32)))",      "optional(seq(tensor(int64)))",  "optional(seq(tensor(bfloat16)))",
         "optional(seq(tensor(float16)))",    "optional(seq(tensor(float)))",  "optional(seq(tensor(double)))",
         "optional(seq(tensor(string)))",     "optional(seq(tensor(bool)))",   "optional(seq(tensor(complex64)))",
         "optional(seq(tensor(complex128)))", "optional(tensor(uint8))",       "optional(tensor(uint16))",
         "optional(tensor(uint32))",          "optional(tensor(uint64))",      "optional(tensor(int8))",
         "optional(tensor(int16))",           "optional(tensor(int32))",       "optional(tensor(int64))",
         "optional(tensor(bfloat16))",        "optional(tensor(float16))",     "optional(tensor(float))",
         "optional(tensor(double))",          "optional(tensor(string))",      "optional(tensor(bool))",
         "optional(tensor(complex64))",       "optional(tensor(complex128))"};
     return all_optional_types;
   }
 
   static const std::vector<std::string>& all_optional_types_ir9() {
     static const std::vector<std::string> all_optional_types = {
         "optional(seq(tensor(uint8)))",      "optional(seq(tensor(uint16)))", "optional(seq(tensor(uint32)))",
         "optional(seq(tensor(uint64)))",     "optional(seq(tensor(int8)))",   "optional(seq(tensor(int16)))",
         "optional(seq(tensor(int32)))",      "optional(seq(tensor(int64)))",  "optional(seq(tensor(bfloat16)))",
         "optional(seq(tensor(float16)))",    "optional(seq(tensor(float)))",  "optional(seq(tensor(double)))",
         "optional(seq(tensor(string)))",     "optional(seq(tensor(bool)))",   "optional(seq(tensor(complex64)))",
         "optional(seq(tensor(complex128)))", "optional(tensor(uint8))",       "optional(tensor(uint16))",
         "optional(tensor(uint32))",          "optional(tensor(uint64))",      "optional(tensor(int8))",
         "optional(tensor(int16))",           "optional(tensor(int32))",       "optional(tensor(int64))",
         "optional(tensor(bfloat16))",        "optional(tensor(float16))",     "optional(tensor(float))",
         "optional(tensor(double))",          "optional(tensor(string))",      "optional(tensor(bool))",
         "optional(tensor(complex64))",       "optional(tensor(complex128))",  "optional(tensor(float8e4m3fn))",
         "optional(tensor(float8e4m3fnuz))",  "optional(tensor(float8e5m2))",  "optional(tensor(float8e5m2fnuz))"};
     return all_optional_types;
   }
 
   static const std::vector<std::string>& all_optional_types_ir10() {
     static const std::vector<std::string> all_optional_types = {
         "optional(seq(tensor(uint8)))",      "optional(seq(tensor(uint16)))", "optional(seq(tensor(uint32)))",
         "optional(seq(tensor(uint64)))",     "optional(seq(tensor(int8)))",   "optional(seq(tensor(int16)))",
         "optional(seq(tensor(int32)))",      "optional(seq(tensor(int64)))",  "optional(seq(tensor(bfloat16)))",
         "optional(seq(tensor(float16)))",    "optional(seq(tensor(float)))",  "optional(seq(tensor(double)))",
         "optional(seq(tensor(string)))",     "optional(seq(tensor(bool)))",   "optional(seq(tensor(complex64)))",
         "optional(seq(tensor(complex128)))", "optional(tensor(uint8))",       "optional(tensor(uint16))",
         "optional(tensor(uint32))",          "optional(tensor(uint64))",      "optional(tensor(int8))",
         "optional(tensor(int16))",           "optional(tensor(int32))",       "optional(tensor(int64))",
         "optional(tensor(bfloat16))",        "optional(tensor(float16))",     "optional(tensor(float))",
         "optional(tensor(double))",          "optional(tensor(string))",      "optional(tensor(bool))",
         "optional(tensor(complex64))",       "optional(tensor(complex128))",  "optional(tensor(float8e4m3fn))",
         "optional(tensor(float8e4m3fnuz))",  "optional(tensor(float8e5m2))",  "optional(tensor(float8e5m2fnuz))",
         "optional(tensor(uint4))",           "optional(tensor(int4))"};
     return all_optional_types;
   }
 
   // Calls the passed function with `this` as an argument. Useful for
   // adding docs for temlated/macro ops.
   OpSchema& FillUsing(const std::function<void(OpSchema&)>& populator);
 
   friend std::ostream& operator<<(std::ostream& out, const OpSchema& schema);
 
   const std::string& domain() const {
     return domain_;
   }
 
   const std::map<std::string, Attribute>& attributes() const {
     return attributes_;
   }
 
   // Get input formal parameters.
   const std::vector<FormalParameter>& inputs() const {
     return inputs_;
   }
 
   // Get output formal parameters.
   const std::vector<FormalParameter>& outputs() const {
     return outputs_;
   }
 
   const std::vector<TypeConstraintParam>& typeConstraintParams() const {
     return type_constraint_params_;
   }
 
   const TypeConstraintMap& typeConstraintMap() const {
     return type_constraints_;
   }
 
   const std::string& Name() const {
     return name_;
   }
 
   OperatorSetVersion SinceVersion() const {
     return since_version_;
   }
 
   int since_version() const {
     return since_version_;
   }
 
   bool deprecated() const {
     return deprecated_;
   }
 
   int min_input() const {
     return min_input_;
   }
   int max_input() const {
     return max_input_;
   }
   int min_output() const {
     return min_output_;
   }
   int max_output() const {
     return max_output_;
   }
 
   bool has_type_and_shape_inference_function() const {
     return tensor_inference_function_ ? true : false;
   }
 
   bool has_data_propagation_function() const {
     return data_propagation_function_ ? true : false;
   }
 
   std::vector<int> function_opset_versions() const {
     std::vector<int> opset_versions;
     std::map<int, std::shared_ptr<FunctionProto>>::const_iterator it = opset_version_to_function_body_.cbegin();
     for (; it != opset_version_to_function_body_.cend(); ++it) {
       opset_versions.push_back(it->first);
     }
     return opset_versions;
   }
 
   bool HasFunction() const {
     return !opset_version_to_function_body_.empty();
   }
 
   OpSchema& FunctionBody(const std::vector<NodeProto>& func_nodes, int opset_version = kUninitializedSinceVersion);
 
   OpSchema& FunctionBody(
       const std::vector<NodeProto>& func_nodes,
       const std::vector<OperatorSetIdProto>& opsets,
       int opset_version = kUninitializedSinceVersion);
 
   OpSchema& FunctionBody(const char* func_body, int opset_version = kUninitializedSinceVersion);
 
   // since_version_ of an OpSchema tells the last opset version when an op is defined.
   // When the op's definition is changed, a new OpSchema (of the same op_type) is created
   // with a newer since_version_, reflecting the opset version at the time of change.
   // For a function op, operators used to define its function body may change
   // while there is no change to the function op definition itself.
   // When this happens, mutiple function bodies are provided, each for a specific opset version.
   //
   // Take LogSoftmax for example. Its latest opset version is 13.
   // In LogSoftmax's function body, ReduceMax (with since_version_ 1, 11, 12, 18) is used.
   // When a model containing LogSoftmax with opset_import version within 13 to 17 is loaded, function body
   // with opset_version 13 is used for inlining.
   // When the same model but opset_import version 18 is loaded, function body
   // with opset_version 18 is used for inlining.
   // Clearly function body for opset_import version 13 will not work
   // in a model with opset_import version 18 because the function body make worng use of ReduceMax(18).
   // Inside GetFunction we ensure that ops being used to construct a function body do not endure such
   // issue.
   const FunctionProto* GetFunction(
       int requested_opset_version = OpSchema::kUninitializedSinceVersion,
       bool validate = false) const;
 
   std::vector<int> context_dependent_function_opset_versions() const {
     std::vector<int> opset_versions;
     std::map<int, ContextDependentFunctionBodyBuilder>::const_iterator it = opset_version_to_function_builder_.cbegin();
     for (; it != opset_version_to_function_builder_.cend(); ++it) {
       opset_versions.push_back(it->first);
     }
     return opset_versions;
   }
 
   bool HasContextDependentFunction() const {
     return !opset_version_to_function_builder_.empty();
   }
 
   bool HasContextDependentFunctionWithOpsetVersion(int opset_version) const {
     return opset_version_to_function_builder_.find(opset_version) != opset_version_to_function_builder_.end();
   }
 
   OpSchema& SetContextDependentFunctionBodyBuilder(
       ContextDependentFunctionBodyBuilder,
       int opset_version = kUninitializedSinceVersion);
 
   bool BuildContextDependentFunction(
       const FunctionBodyBuildContext& ctx,
       FunctionProto& function_proto,
       int requested_opset_version = OpSchema::kUninitializedSinceVersion) const;
 
   // Verifies that the schema is valid and all specifications are compatible.
   // It will also parse all type strings specified for inputs/outputs into valid
   // TypeProto and create global unique string pointer as the DataType for
   // efficiency.
   void Finalize();
 
   // Build function with information stored in opschema
   void BuildFunction(FunctionProto& function_body) const;
 
  private:
   void ParseAndSetTypes(
       /*out*/ std::vector<OpSchema::FormalParameter>* formalParameters);
   bool ValidateReferencedOpsInFuncton(
       const FunctionProto* function,
       int requested_opset_version,
       int function_since_version,
       std::set<std::string>* updated_ops = nullptr) const;
   void UpdateFunctionProtoOpsetImportVersion(FunctionProto& function_proto, int opset_version) const;
 
   /**
    * @brief A common function to generate a prefix string for use in fail_check during the verify function.
    * @param  node_name If empty, the returned string will not include the node name.
    * @return std::string The prefix string.
    */
   std::string VerifyFailPrefix(std::string_view node_name) const;
 
   /**
    * @brief Verifies if the input number matches the pattern specified in the schema.
    * @param input_num The number of inputs to be verified against the schema.
    * @param node_info The prefix string used if the check fails.
    */
   void VerifyInputNum(int input_num, std::string_view node_name = "") const;
 
   /**
    * @brief Verifies if the output number matches the pattern specified in the schema.
    * @param output_num The number of outputs to be verified against the schema.
    * @param node_info The prefix string used if the check fails.
    */
   void VerifyOutputNum(int output_num, std::string_view node_name = "") const;
 
   std::string name_;
   std::string file_;
   std::string doc_;
   // Default domain value ("") means it's ONNX domain.
   std::string domain_ = ONNX_DOMAIN;
   std::map<std::string, Attribute> attributes_{};
   bool allows_unchecked_attributes_ = false;
   std::vector<FormalParameter> inputs_;
   std::vector<FormalParameter> outputs_;
   std::vector<TypeConstraintParam> type_constraint_params_;
   TypeConstraintMap type_constraints_;
   int line_ = 0;
   SupportType support_;
   int min_input_ = 0;
   int max_input_ = 0;
   int min_output_ = 0;
   int max_output_ = 0;
   // The default is a little goofy, since it is never what you want
   OperatorSetVersion since_version_ = kUninitializedSinceVersion;
   bool deprecated_{};
   std::function<bool(int)> num_inputs_allowed_ = [](int) { return true; };
   std::function<bool(int)> num_outputs_allowed_ = [](int) { return true; };
   InferenceFunction tensor_inference_function_;
   DataPropagationFunction data_propagation_function_;
 
   std::map<int, std::shared_ptr<FunctionProto>> opset_version_to_function_body_;
   std::map<int, ContextDependentFunctionBodyBuilder> opset_version_to_function_builder_;
 };
 
 // Map type to store operator schemas. The format is,
 // <OpName, <Domain, <OperatorSetVersion, OpSchema>>>.
 using OpName_Domain_Version_Schema_Map =
     std::unordered_map<std::string, std::unordered_map<std::string, std::map<OperatorSetVersion, OpSchema>>>;
 
 class ISchemaRegistry {
  public:
   virtual ~ISchemaRegistry() = default;
 
   virtual const OpSchema*
   GetSchema(const std::string& key, const int maxInclusiveVersion, const std::string& domain = ONNX_DOMAIN) const = 0;
 };
 
 /**
  * @brief A registry to hold all the operator schemas.
  */
 class OpSchemaRegistry final : public ISchemaRegistry {
  public:
   // A singleton class to store domain to min/max op_set version map, as well as
   // domain to last-release op_set version map.
   class DomainToVersionRange final {
    public:
     DomainToVersionRange() {
       // Increase the highest version when you make BC-breaking changes to the
       // operator schema on specific domain. Update the lowest version when it's
       // determined to remove too old version history.
       map_[ONNX_DOMAIN] = std::make_pair(1, 22);
       map_[AI_ONNX_ML_DOMAIN] = std::make_pair(1, 5);
       map_[AI_ONNX_TRAINING_DOMAIN] = std::make_pair(1, 1);
       // ONNX's preview domain contains operators subject to change, so
       // versining is not meaningful and that domain should have only one
       // version.
       map_[AI_ONNX_PREVIEW_TRAINING_DOMAIN] = std::make_pair(1, 1);
       // Version corresponding last release of ONNX. Update this to match with
       // the max version above in a *release* version of ONNX. But in other
       // versions, the max version may be ahead of the last-release-version.
       last_release_version_map_[ONNX_DOMAIN] = 22;
       last_release_version_map_[AI_ONNX_ML_DOMAIN] = 5;
       last_release_version_map_[AI_ONNX_TRAINING_DOMAIN] = 1;
       last_release_version_map_[AI_ONNX_PREVIEW_TRAINING_DOMAIN] = 1;
     }
 
     const std::unordered_map<std::string, std::pair<int, int>>& Map() const {
       return map_;
     }
 
     const std::unordered_map<std::string, int>& LastReleaseVersionMap() const {
       return last_release_version_map_;
     }
 
     // Add customized domain to min/max version.
     // Onnx partners are able to use onnx operator schema api to
     // register customized op in their own domain.
     // Can optionally specify last_release_version (to make it similar to
     // standard ONNX domains as above). Custom-domains are free to interpret
     // this as appropriate (that is, as relative to releases of custom-domain
     // as opposed to ONNX releases).
     void
     AddDomainToVersion(const std::string& domain, int min_version, int max_version, int last_release_version = -1) {
       std::lock_guard<std::mutex> lock(mutex_);
       if (map_.count(domain) != 0) {
         std::stringstream err;
         err << "Trying to add a domain to DomainToVersion map, but the domain is already exist with version range ("
             << map_.at(domain).first << ", " << map_.at(domain).second << "). domain: \"" << domain << "\""
             << std::endl;
         fail_schema(err.str());
       }
       if (last_release_version_map_.count(domain) != 0) {
         std::stringstream err;
         err << "Trying to add a domain to LastReleaseVersion map, but the domain is already exist with last version: "
             << last_release_version_map_.at(domain) << ", domain: \"" << domain << "\"" << std::endl;
         fail_schema(err.str());
       }
       map_[domain] = std::make_pair(min_version, max_version);
       // If a last-release-version is not explicitly specified, use max as
       // last-release-version.
       if (last_release_version == -1) {
         last_release_version = max_version;
       }
       last_release_version_map_[domain] = last_release_version;
     }
 
     void
     UpdateDomainToVersion(const std::string& domain, int min_version, int max_version, int last_release_version = -1) {
       std::lock_guard<std::mutex> lock(mutex_);
       if (map_.count(domain) == 0) {
         std::stringstream err;
         err << "Trying to update a domain in DomainToVersion map, but the domain has not been add. domain: \"" << domain
             << "\"" << std::endl;
         fail_schema(err.str());
       }
       if (last_release_version_map_.count(domain) == 0) {
         std::stringstream err;
         err << "Trying to update a domain in LastReleaseVersion map, but the domain has not been add. domain: \""
             << domain << "\"" << std::endl;
         fail_schema(err.str());
       }
       map_.at(domain).first = min_version;
       map_.at(domain).second = max_version;
       // Correspond to `AddDomainToVersion`
       if (last_release_version == -1) {
         last_release_version = max_version;
       }
       last_release_version_map_.at(domain) = last_release_version;
     }
 
     static DomainToVersionRange& Instance();
 
    private:
     // Key: domain. Value: <lowest version, highest version> pair.
     std::unordered_map<std::string, std::pair<int, int>> map_;
 
     // Key: domain. Value: most recent release opset version. Note that
     // the highest opset version may be ahead of the most recent release's opset
     // version.
     std::unordered_map<std::string, int> last_release_version_map_;
 
     std::mutex mutex_;
   };
 
   class OpSchemaRegisterOnce final {
    public:
     // Export to cpp custom register macro
     OpSchemaRegisterOnce(OpSchema op_schema, int opset_version_to_load = 0, bool fail_duplicate_schema = true) {
       OpSchemaRegisterNoExcept(std::move(op_schema), opset_version_to_load, fail_duplicate_schema);
     }
     static void
     OpSchemaRegisterNoExcept(OpSchema&& op_schema, int opset_version_to_load = 0, bool fail_duplicate_schema = true) {
       ONNX_TRY {
         OpSchemaRegisterImpl(std::move(op_schema), opset_version_to_load, fail_duplicate_schema);
       }
       ONNX_CATCH(const std::exception& e) {
         ONNX_HANDLE_EXCEPTION([&]() { std::cerr << "Schema error: " << e.what() << std::endl; });
       }
     }
     static void
     OpSchemaRegisterImpl(OpSchema&& op_schema, int opset_version_to_load = 0, bool fail_duplicate_schema = true) {
       op_schema.Finalize();
       auto& m = GetMapWithoutEnsuringRegistration();
       auto& op_name = op_schema.Name();
       auto& op_domain = op_schema.domain();
       auto& schema_ver_map = m[op_name][op_domain];
       auto ver = op_schema.SinceVersion();
       if (OpSchema::kUninitializedSinceVersion == ver) {
         op_schema.SinceVersion(1);
         ver = op_schema.SinceVersion();
       }
 
       // Stops because the exact opset_version is registered
       if (schema_ver_map.count(ver)) {
         if (fail_duplicate_schema) {
           const auto& schema = schema_ver_map[ver];
           std::stringstream err;
           err << "Trying to register schema with name " << op_name << " (domain: " << op_domain << " version: " << ver
               << ") from file " << op_schema.file() << " line " << op_schema.line()
               << ", but it is already registered from file " << schema.file() << " line " << schema.line() << std::endl;
           fail_schema(err.str());
         }
         return;
       }
 
       if (opset_version_to_load != 0) {
         // Stops because the opset_version is higher than opset_version_to_load
         if (ver > opset_version_to_load) {
           return;
         }
 
         // Stops because a later version is registered within target opset version
         if (!schema_ver_map.empty()) {
           int max_registered_ver_le_target = GetMaxRegisteredVerWithinTarget(schema_ver_map, opset_version_to_load);
           if (max_registered_ver_le_target >= ver) {
             return;
           }
         }
       }
 
       CheckDomainAndVersionToRegister(op_schema, op_name, op_domain);
       schema_ver_map.insert(std::pair<int, OpSchema&&>(ver, std::move(op_schema)));
     }
 
    private:
     // Gets the maximum version from given map that is less or equal to target version
     static int GetMaxRegisteredVerWithinTarget(const std::map<OperatorSetVersion, OpSchema>& m, int target_ver) {
       // std::map is sorted on key
       // reverse iterator returns the largest element keyed on the integer version
       for (auto&& it = m.rbegin(); it != m.rend(); it++) {
         const auto& registered_ver = it->first;
         if (registered_ver <= target_ver) {
           return registered_ver;
         }
       }
       return -1;
     }
 
     static void CheckDomainAndVersionToRegister(
         const OpSchema& op_schema,
         const std::string& op_name,
         const std::string& op_domain) {
       auto ver_range_map = DomainToVersionRange::Instance().Map();
       auto ver_range_it = ver_range_map.find(op_domain);
       auto ver = op_schema.SinceVersion();
 
       if (ver_range_it == ver_range_map.end()) {
         std::stringstream err;
         err << "Trying to register schema with name " << op_name << " (domain: " << op_domain << " version: " << ver
             << ") from file " << op_schema.file() << " line " << op_schema.line() << ", but its domain is not"
             << " known by the checker." << std::endl;
 
         fail_schema(err.str());
       }
       auto lower_bound_incl = ver_range_it->second.first;
       auto upper_bound_incl = ver_range_it->second.second;
       if (!(lower_bound_incl <= ver && upper_bound_incl >= ver)) {
         std::stringstream err;
         err << "Trying to register schema with name " << op_name << " (domain: " << op_domain << " version: " << ver
             << ") from file " << op_schema.file() << " line " << op_schema.line() << ", but its version is not "
             << "in the inclusive range [" << lower_bound_incl << ", " << upper_bound_incl
             << "] (usually, this means you "
             << "bumped the operator version but "
             << "forgot to update the version range in DomainToVersionRange "
             << "in onnx/defs/schema.h)." << std::endl;
         fail_schema(err.str());
       }
     }
   };
 
   static void
   OpSchemaDeregister(const std::string& op_type, const int version, const std::string& domain = ONNX_DOMAIN) {
     auto& schema_map = GetMapWithoutEnsuringRegistration();
     if (schema_map.count(op_type) && schema_map[op_type].count(domain) && schema_map[op_type][domain].count(version)) {
       schema_map[op_type][domain].erase(version);
     } else {
       std::stringstream err;
       err << "Attempting to deregister an unregistered schema with name: " << op_type << " domain: " << domain
           << " version: " << version << std::endl;
       fail_schema(err.str());
     }
   }
 
   // Deregister all ONNX opset schemas from domain
   // Domain with default value ONNX_DOMAIN means ONNX.
   static void OpSchemaDeregisterAll(const std::string& domain = ONNX_DOMAIN) {
     auto& schema_map = GetMapWithoutEnsuringRegistration();
     // schema_map stores operator schemas in the format of
     // <OpName, <Domain, <OperatorSetVersion, OpSchema>>>
     for (auto&& schema_map_pair : schema_map) {
       auto& domain_map = schema_map_pair.second;
       if (domain_map.count(domain)) {
         auto& opset_version_schema_map = domain_map[domain];
         // Invalidates ver-schema pairs and frees memory, leaving m[op_name][op_domain] empty
         opset_version_schema_map.clear();
         domain_map.erase(domain);
       }
     }
   }
 
   // Return the latest schema for an operator in specified domain.
   // Domain with default value ONNX_DOMAIN means ONNX.
   static const OpSchema* Schema(const std::string& key, const std::string& domain = ONNX_DOMAIN) {
     auto& m = map();
     if (m.count(key) && m[key].count(domain)) {
       const auto& schema_ver_map = m[key][domain];
       if (!schema_ver_map.empty()) {
         return &m[key][domain].rbegin()->second;
       }
     }
     return nullptr;
   }
 
   // Return the schema with biggest version, which is not greater than specified
   // <maxInclusiveVersion> in specified domain. Domain with default value
   // ONNX_DOMAIN means ONNX.
   static const OpSchema*
   Schema(const std::string& key, const int maxInclusiveVersion, const std::string& domain = ONNX_DOMAIN) {
     auto& m = map();
     if (m.count(key) && m[key].count(domain)) {
       const auto& schema_ver_map = m[key][domain];
       if (!schema_ver_map.empty()) {
         auto pos = m[key][domain].lower_bound(maxInclusiveVersion);
         if (m[key][domain].begin() == pos && pos->first > maxInclusiveVersion) {
           // All versions are greater than specified version.
           return nullptr;
         }
         if (m[key][domain].end() == pos || pos->first > maxInclusiveVersion) {
           // All versions are less than specified version, or,
           // The <pos> version is greater than specified version.
           pos--;
         }
 
         // Schema with exact version as specified one exists.
         return &(pos->second);
       }
     }
     return nullptr;
   }
 
   static OpSchemaRegistry* Instance();
 
   const OpSchema* GetSchema(
       const std::string& key,
       const int maxInclusiveVersion,
       const std::string& domain = ONNX_DOMAIN) const override {
     return Schema(key, maxInclusiveVersion, domain);
   }
   static void SetLoadedSchemaVersion(int target_version) {
     loaded_schema_version = target_version;
   }
   static int GetLoadedSchemaVersion() {
     return loaded_schema_version;
   }
 
  private:
   // OpSchemaRegistry should not need to be instantiated except statically
   // within this class
   OpSchemaRegistry() = default;
 
   /**
    * @brief Returns the underlying string to OpSchema map.
    *
    * You should not manually manipulate the map object returned. Instead, use
    * the macros defined such as ONNX_OPERATOR_SET_SCHEMA to register your
    * operator schema.
    *
    * We wrap it inside a function to avoid the static initialization order
    * fiasco.
    */
   static OpName_Domain_Version_Schema_Map& GetMapWithoutEnsuringRegistration();
   static OpName_Domain_Version_Schema_Map& map();
   static int loaded_schema_version;
 
  public:
   static const std::vector<OpSchema> get_all_schemas_with_history() {
     std::vector<OpSchema> r;
     for (auto& x : map()) {
       for (auto& y : x.second) {
         for (auto& z : y.second) {
           r.emplace_back(z.second);
         }
       }
     }
     return r;
   }
 
   static const std::vector<OpSchema> get_all_schemas() {
     std::vector<OpSchema> r;
     for (auto& x : map()) {
       for (auto& y : x.second) {
         auto& version2schema = y.second;
         if (!version2schema.empty()) {
           r.emplace_back(version2schema.rbegin()->second);
         }
       }
     }
     return r;
   }
 };
 
 void RegisterSchema(
     const OpSchema& schema,
     int opset_version_to_load = 0,
     bool fail_duplicate_schema = true,
     bool fail_with_exception = false);
 void RegisterSchema(
     OpSchema&& schema,
     int opset_version_to_load = 0,
     bool fail_duplicate_schema = true,
     bool fail_with_exception = false);
 void DeregisterSchema(const std::string& op_type, int version, const std::string& domain);
 
 // Registers the latest opset schema before opset_version_to_load
 // By default opset_version_to_load=0 means it will register all versions
 template <class T>
 void RegisterOpSetSchema(int opset_version_to_load = 0, bool fail_duplicate_schema = true) {
   T::ForEachSchema([opset_version_to_load, fail_duplicate_schema](OpSchema&& schema) {
     RegisterSchema(std::move(schema), opset_version_to_load, fail_duplicate_schema);
   });
 };
 
 // Forward declaration for the non-specialized GetOpSchema method.  This
 // enforces a consistent signature on functions that query individual schema,
 // which are defined as specializations of this function.
 template <typename T>
 OpSchema GetOpSchema();
 
 #define ONNX_OPERATOR_SET_SCHEMA(name, ver, impl) ONNX_OPERATOR_SET_SCHEMA_EX(name, Onnx, ONNX_DOMAIN, ver, true, impl)
 
 #define ONNX_ML_OPERATOR_SET_SCHEMA(name, ver, impl) \
   ONNX_OPERATOR_SET_SCHEMA_EX(name, OnnxML, AI_ONNX_ML_DOMAIN, ver, true, impl)
 
 #define ONNX_TRAINING_OPERATOR_SET_SCHEMA(name, ver, impl) \
   ONNX_OPERATOR_SET_SCHEMA_EX(name, OnnxTraining, AI_ONNX_TRAINING_DOMAIN, ver, true, impl)
 
 #define ONNX_PREVIEW_TRAINING_OPERATOR_SET_SCHEMA(name, ver, impl) \
   ONNX_OPERATOR_SET_SCHEMA_EX(name, OnnxPreview, AI_ONNX_PREVIEW_TRAINING_DOMAIN, ver, true, impl)
 
 // Defines specialization of GetOpSchema for a class whose name is determined
 // based on a convention using name, domain, and version.  Operator schema are
 // normally included in operator sets and registered in OpSchemaRegistry::map().
 // In this case, callers should set dbg_included_in_static_opset to true.  This
 // assists with runtime validation in DEBUG builds ensuring the intended set
 // of operator schema is registered.
 #define ONNX_OPERATOR_SET_SCHEMA_EX(name, domain, domain_str, ver, dbg_included_in_static_opset, impl)  \
   class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(domain, ver, name);                                         \
   template <>                                                                                           \
   OpSchema GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(domain, ver, name)>() {                      \
     return impl.SetName(#name).SetDomain(domain_str).SinceVersion(ver).SetLocation(__FILE__, __LINE__); \
   }                                                                                                     \
   size_t dbg_count_check_##name##_##domain##_ver##ver =                                                 \
       (dbg_included_in_static_opset) ? ONNX_DBG_INCREMENT_COUNT_IN_OPSETS() : 0;
 #ifdef NDEBUG
 #define ONNX_DBG_INCREMENT_COUNT_IN_OPSETS() 0
 #else
 #define ONNX_DBG_INCREMENT_COUNT_IN_OPSETS() DbgOperatorSetTracker::Instance().IncrementCount()
 #define ONNX_DBG_GET_COUNT_IN_OPSETS() DbgOperatorSetTracker::Instance().GetCount()
 
 class DbgOperatorSetTracker {
  public:
   static DbgOperatorSetTracker& Instance();
 
   size_t IncrementCount() {
     return ++count_;
   }
 
   size_t GetCount() const {
     return count_;
   }
 
  private:
   size_t count_ = 0;
 };
 #endif
 
 // Naming convention for operator schema classes
 #define ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(domain, ver, name) name##_##domain##_ver##ver
 
 // Naming convention for preview operator schema classes
 #define ONNX_PREVIEW_OPERATOR_SET_SCHEMA_CLASS_NAME(ver, name) \
   ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxPreview, ver, name)
 
 // Helper function
 size_t ReplaceAll(std::string& s, const char* from, const char* to);
 
 #ifdef __GNUC__
 #define ONNX_UNUSED __attribute__((__unused__))
 #else
 #define ONNX_UNUSED
 #endif
 
 // Legacy macros to register schema at static initialization
 #define ONNX_OPERATOR_SCHEMA(name) ONNX_OPERATOR_SCHEMA_UNIQ_HELPER(__COUNTER__, name)
 #define ONNX_OPERATOR_SCHEMA_UNIQ_HELPER(Counter, name) ONNX_OPERATOR_SCHEMA_UNIQ(Counter, name)
 #define ONNX_OPERATOR_SCHEMA_UNIQ(Counter, name)                                                                      \
   static ONNX_NAMESPACE::OpSchemaRegistry::OpSchemaRegisterOnce(op_schema_register_once##name##Counter) ONNX_UNUSED = \
       OpSchema(#name, __FILE__, __LINE__)
 
 // Helper function
 size_t ReplaceAll(std::string& s, const char* from, const char* to);
 
 inline std::string GenerateOptionalArgumentsDoc() {
   return "This operator has **optional** inputs/outputs. "
          "See [the doc](IR.md) for more details about the representation of "
          "optional arguments. An empty string may be used in the place of "
          "an actual argument's name to indicate a missing argument. "
          "Trailing optional arguments (those not followed by an argument "
          "that is present) may also be simply omitted.\n";
 }
 
 inline std::string GenerateBroadcastingDocMul() {
   return "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**;"
          " for more details please check [the doc](Broadcasting.md).";
 }
 
 inline std::string GenerateBroadcastingDocUni(const char* from, const char* to) {
   std::string ret = "This operator supports **unidirectional broadcasting** (";
   ret = ret + from + " should be unidirectional broadcastable to " + to +
       ");"
       " for more details please check [the doc](Broadcasting.md).";
   return ret;
 }
 
 /*
  * Macros for setting operator documentation
  * Use this macro for simple SetDoc() calls that generate documentation
  * directly. This is the macro to use in almost all cases.
  * Sample usage guidelines:
  * const char* doc_str = "foo";
  * SetDoc(GET_OP_DOC_STR(doc_str))
  *
  * SetDoc(GET_OP_DOC_STR(
             std::string(BitShift_ver11_doc) + GenerateBroadcastingDocMul()))
  */
 #ifndef __ONNX_NO_DOC_STRINGS
 #define GET_OP_DOC_STR(doc_str) (doc_str)
 #else
 #define GET_OP_DOC_STR(doc_str) ("")
 #endif
 
 /*
  * Use this macro when the documentation needs to be populated in some
  * complicated way like string substitutions, etc before calling SetDoc.
  * Sample usage guidelines:
     std::string doc;
     POPULATE_OP_DOC_STR(
         doc = R"DOC(
 Returns the tensor resulted from performing the `{name}` logical operation
 elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting
 support).
 
 {broadcast_doc}
 )DOC";
         ReplaceAll(doc, "{name}", name);
         ReplaceAll(
             doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
     schema.SetDoc(doc);
  *
  */
 #ifndef __ONNX_NO_DOC_STRINGS
 #define POPULATE_OP_DOC_STR(DocPopulatorCode) \
   do {                                        \
     DocPopulatorCode                          \
   } while (0)
 #else
 #define POPULATE_OP_DOC_STR(DocPopulatorCode)
 #endif
 
 } // namespace ONNX_NAMESPACE

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