// Generated by tfcompile, the TensorFlow graph compiler.  DO NOT EDIT!
//
// This header was generated via ahead-of-time compilation of a TensorFlow
// graph.  An object file corresponding to this header was also generated.
// This header gives access to the functionality in that object file.
//
// clang-format off

#ifndef TFCOMPILE_GENERATED_entry_point_H_  // NOLINT(build/header_guard)
#define TFCOMPILE_GENERATED_entry_point_H_  // NOLINT(build/header_guard)

#include "tensorflow/compiler/xla/xla_data.pb.h"

#include "tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h"
#include "tensorflow/core/platform/types.h"

namespace Eigen { struct ThreadPoolDevice; }
namespace xla { class ExecutableRunOptions; }

// (Implementation detail) Entry point to the function in the object file.
extern "C" void entry_point(
    void* result, const xla::ExecutableRunOptions* run_options,
    const void** args, void** temps, tensorflow::int64* profile_counters);

extern "C" char __tfcompile_foo_bar_MyClass_ProgramShape_protobuf_array_contents[];


namespace foo {
namespace bar {

// MyClass represents a computation previously specified in a
// TensorFlow graph, now compiled into executable code. This extends the generic
// XlaCompiledCpuFunction class with statically type-safe arg and result
// methods. Usage example:
//
//   MyClass computation;
//   // ...set args using computation.argN methods
//   CHECK(computation.Run());
//   // ...inspect results using computation.resultN methods
//
// The Run method invokes the actual computation, with inputs read from arg
// buffers, and outputs written to result buffers. Each Run call may also use
// a set of temporary buffers for the computation.
//
// By default each instance of this class manages its own arg, result and temp
// buffers. The AllocMode constructor parameter may be used to modify the
// buffer allocation strategy.
//
// Under the default allocation strategy, this class is thread-compatible:
// o Calls to non-const methods require exclusive access to the object.
// o Concurrent calls to const methods are OK, if those calls are made while it
//   is guaranteed that no thread may call a non-const method.
//
// The logical function signature is:
//   ((unknown): f32[1,2], (unknown): s64[3,4]) -> (u32[5,6])
//
// Memory stats:
//   arg bytes total:    104
//   arg bytes aligned:  192
//   temp bytes total:   126
//   temp bytes aligned: 320
class MyClass : public tensorflow::XlaCompiledCpuFunction {
 public:
  // Number of input arguments for the compiled computation.
  static constexpr size_t kNumArgs = 2;

  // Byte size of each argument buffer. There are kNumArgs entries.
  static const ::tensorflow::int64 ArgSize(::tensorflow::int32 index) {
    return BufferInfos()[ArgIndexToBufferIndex()[index]].size();
  }

  // Returns static data used to create an XlaCompiledCpuFunction.
  static const tensorflow::XlaCompiledCpuFunction::StaticData& StaticData() {
    static XlaCompiledCpuFunction::StaticData* kStaticData = [](){
      XlaCompiledCpuFunction::StaticData* data =
        new XlaCompiledCpuFunction::StaticData;
      data->set_raw_function(entry_point);
      data->set_buffer_infos(BufferInfos());
      data->set_num_buffers(kNumBuffers);
      data->set_arg_index_table(ArgIndexToBufferIndex());
      data->set_num_args(kNumArgs);
      data->set_result_index(kResultIndex);
      data->set_arg_names(StaticArgNames());
      data->set_result_names(StaticResultNames());
      data->set_program_shape(StaticProgramShape());
      data->set_hlo_profile_printer_data(StaticHloProfilePrinterData());

      return data;
    }();
    return *kStaticData;
  }

  MyClass(AllocMode alloc_mode = AllocMode::ARGS_RESULTS_PROFILES_AND_TEMPS)
      : XlaCompiledCpuFunction(StaticData(), alloc_mode) {}

  MyClass(const MyClass&) = delete;
  MyClass& operator=(const MyClass&) = delete;

  // Arg methods for managing input buffers. Buffers are in row-major order.
  // There is a set of methods for each positional argument, with the following
  // general form:
  //
  // void set_argN_data(void* data)
  //   Sets the buffer of type T for positional argument N. May be called in
  //   any AllocMode. Must be called before Run to have an affect. Must be
  //   called in AllocMode::RESULTS_PROFILES_AND_TEMPS_ONLY for each positional
  //   argument, to set the argument buffers.
  //
  // T* argN_data()
  //   Returns the buffer of type T for positional argument N.
  //
  // T& argN(...dim indices...)
  //   Returns a reference to the value of type T for positional argument N,
  //   with dim indices specifying which value. No bounds checking is performed
  //   on dim indices.

  void set_arg0_data(void* data) {
    set_arg_data(0, data);
  }
  float* arg0_data() {
    return static_cast<float*>(arg_data(0));
  }
  float& arg0(size_t dim0, size_t dim1) {
    return (*static_cast<float(*)[1][2]>(
        arg_data(0)))[dim0][dim1];
  }
  const float* arg0_data() const {
    return static_cast<const float*>(arg_data(0));
  }
  const float& arg0(size_t dim0, size_t dim1) const {
    return (*static_cast<const float(*)[1][2]>(
        arg_data(0)))[dim0][dim1];
  }

  void set_arg_myfeed_data(void* data) {
    set_arg_data(0, data);
  }
  float* arg_myfeed_data() {
    return static_cast<float*>(arg_data(0));
  }
  float& arg_myfeed(size_t dim0, size_t dim1) {
    return (*static_cast<float(*)[1][2]>(
        arg_data(0)))[dim0][dim1];
  }
  const float* arg_myfeed_data() const {
    return static_cast<const float*>(arg_data(0));
  }
  const float& arg_myfeed(size_t dim0, size_t dim1) const {
    return (*static_cast<const float(*)[1][2]>(
        arg_data(0)))[dim0][dim1];
  }

  void set_arg1_data(void* data) {
    set_arg_data(1, data);
  }
  tensorflow::int64* arg1_data() {
    return static_cast<tensorflow::int64*>(arg_data(1));
  }
  tensorflow::int64& arg1(size_t dim0, size_t dim1) {
    return (*static_cast<tensorflow::int64(*)[3][4]>(
        arg_data(1)))[dim0][dim1];
  }
  const tensorflow::int64* arg1_data() const {
    return static_cast<const tensorflow::int64*>(arg_data(1));
  }
  const tensorflow::int64& arg1(size_t dim0, size_t dim1) const {
    return (*static_cast<const tensorflow::int64(*)[3][4]>(
        arg_data(1)))[dim0][dim1];
  }

  // Result methods for managing output buffers. Buffers are in row-major order.
  // Must only be called after a successful Run call. There is a set of methods
  // for each positional result, with the following general form:
  //
  // T* resultN_data()
  //   Returns the buffer of type T for positional result N.
  //
  // T& resultN(...dim indices...)
  //   Returns a reference to the value of type T for positional result N,
  //   with dim indices specifying which value. No bounds checking is performed
  //   on dim indices.
  //
  // Unlike the arg methods, there is no set_resultN_data method. The result
  // buffers are managed internally, and may change after each call to Run.

  tensorflow::uint32* result0_data() {
    return static_cast<tensorflow::uint32*>(result_data(0));
  }
  tensorflow::uint32& result0(size_t dim0, size_t dim1) {
    return (*static_cast<tensorflow::uint32(*)[5][6]>(
        result_data(0)))[dim0][dim1];
  }
  const tensorflow::uint32* result0_data() const {
    return static_cast<const tensorflow::uint32*>(result_data(0));
  }
  const tensorflow::uint32& result0(size_t dim0, size_t dim1) const {
    return (*static_cast<const tensorflow::uint32(*)[5][6]>(
        result_data(0)))[dim0][dim1];
  }

  tensorflow::uint32* result_myfetch_data() {
    return static_cast<tensorflow::uint32*>(result_data(0));
  }
  tensorflow::uint32& result_myfetch(size_t dim0, size_t dim1) {
    return (*static_cast<tensorflow::uint32(*)[5][6]>(
        result_data(0)))[dim0][dim1];
  }
  const tensorflow::uint32* result_myfetch_data() const {
    return static_cast<const tensorflow::uint32*>(result_data(0));
  }
  const tensorflow::uint32& result_myfetch(size_t dim0, size_t dim1) const {
    return (*static_cast<const tensorflow::uint32(*)[5][6]>(
        result_data(0)))[dim0][dim1];
  }

 private:
  // Number of buffers for the compiled computation.
  static constexpr size_t kNumBuffers = 6;

  static const ::tensorflow::cpu_function_runtime::BufferInfo* BufferInfos() {
    static const ::tensorflow::cpu_function_runtime::BufferInfo
      kBufferInfos[kNumBuffers] = {
::tensorflow::cpu_function_runtime::BufferInfo({5ULL, ~0ULL}),
::tensorflow::cpu_function_runtime::BufferInfo({34ULL, 0ULL}),
::tensorflow::cpu_function_runtime::BufferInfo({9ULL, ~0ULL}),
::tensorflow::cpu_function_runtime::BufferInfo({386ULL, 1ULL}),
::tensorflow::cpu_function_runtime::BufferInfo({13ULL, ~0ULL}),
::tensorflow::cpu_function_runtime::BufferInfo({481ULL, ~0ULL})
      };
    return kBufferInfos;
  }

  static const ::tensorflow::int32* ArgIndexToBufferIndex() {
    static constexpr ::tensorflow::int32 kArgIndexToBufferIndex[kNumArgs] = {
1, 3
    };
    return kArgIndexToBufferIndex;
  }

  // The 0-based index of the result tuple in the temporary buffers.
  static constexpr size_t kResultIndex = 5;

  // Array of names of each positional argument, terminated by nullptr.
  static const char** StaticArgNames() {
    static const char* kNames[] = {"myfeed", nullptr};
    return kNames;
  }

  // Array of names of each positional result, terminated by nullptr.
  static const char** StaticResultNames() {
    static const char* kNames[] = {"myfetch", nullptr};
    return kNames;
  }

  // Shape of the args and results.
  static const xla::ProgramShape* StaticProgramShape() {
    static const xla::ProgramShape* kShape = []() {
    xla::ProgramShape* proto = new xla::ProgramShape;
    proto->ParseFromArray(&__tfcompile_foo_bar_MyClass_ProgramShape_protobuf_array_contents[0], 52);
    return proto;
  }();
    return kShape;
  }

  // Metadata that can be used to pretty-print profile counters.
  static const xla::HloProfilePrinterData* StaticHloProfilePrinterData() {
    static const xla::HloProfilePrinterData* kHloProfilePrinterData =
      nullptr;
    return kHloProfilePrinterData;
  }
};

}  // end namespace bar
}  // end namespace foo

#endif  // TFCOMPILE_GENERATED_entry_point_H_

// clang-format on