const char* name = "split3");

bool IsPlaceholder(const tensorflow::NodeDef& node_def);

bool IsScalarConst(const tensorflow::NodeDef& node_def, int v);

bool IsAddN(const tensorflow::NodeDef& node_def, int n);

bool IsNeg(const tensorflow::NodeDef& node_def, const string& input);

bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def);

  virtual void SetAllowSoftPlacement(bool enable) = 0;

  // Configure device placement policy logging.
  virtual void SetLogDevicePlacement(bool enable) = 0;

  // Enables running eager ops as functions.
  virtual void SetRunEagerOpAsFunction(bool enable) = 0;

  // Enables rewriting jit_compile functions.
  virtual void SetJitCompileRewrite(bool enable) = 0;

// functionality of `gradient_checker` by comparing the result with expected
// manual user-provided gradients.
class GradientCheckerTest
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());
    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());

    {
      Status s = StatusFromTF_Status(status.get());

                   "    }"),
      &def));
  return def.SerializeAsString();
}

void TestRemoteExecuteSilentCopies(bool async, bool remote, bool func,
                                   bool heavy_load_on_streaming_rpc,
                                   bool remote_func_outputs,
                                   bool has_packed_input) {
  CHECK(!has_packed_input || func);
  tensorflow::ServerDef server_def = GetServerDef(3);

                         int num_values) override {
    std::unique_ptr<bool[]> b(new bool[num_values]);
    for (int i = 0; i < num_values; ++i) {
      b[i] = values[i];
    }
    op_->node_builder.Attr(attr_name,
                           ArraySlice<const bool>(b.get(), num_values));

    return absl::OkStatus();
  }

typedef struct GCSFile {
  const std::string path;
  const bool is_cache_enable;
  const uint64_t buffer_size;
  ReadFn read_fn;
  absl::Mutex buffer_mutex;
  uint64_t buffer_start ABSL_GUARDED_BY(buffer_mutex);
  bool buffer_end_is_past_eof ABSL_GUARDED_BY(buffer_mutex);
  std::string buffer ABSL_GUARDED_BY(buffer_mutex);

  GCSFile(std::string path, bool is_cache_enable, uint64_t buffer_size,
          ReadFn read_fn)

class UnifiedAPI
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());
    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());
    Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }

 public:
  bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; }

#define TENSORFLOW_C_EAGER_TFE_EXECUTOR_INTERNAL_H_

#include <memory>

#include "tensorflow/core/common_runtime/eager/eager_executor.h"

struct TFE_Executor {
  explicit TFE_Executor(bool async, bool enable_streaming_enqueue,
                        int in_flight_nodes_limit)
      : owned_executor(new tensorflow::EagerExecutor(
            async, enable_streaming_enqueue, in_flight_nodes_limit)) {}

class TracingTensorHandle : public AbstractTensorHandle {
 protected:
  explicit TracingTensorHandle(AbstractTensorHandleKind kind)
      : AbstractTensorHandle(kind) {}

 public:
  // For LLVM style RTTI.
  static bool classof(const AbstractTensorHandle* ptr) {
    return ptr->getKind() == kGraph || ptr->getKind() == kMlir;
  }
};

// An abstract operation describes an operation by its type, name, and

}

TFE_Executor* TFE_NewExecutor(bool is_async, bool enable_streaming_enqueue,
                              int in_flight_nodes_limit) {
  return new TFE_Executor(is_async, enable_streaming_enqueue,
                          in_flight_nodes_limit);
}

void TFE_DeleteExecutor(TFE_Executor* executor) { delete executor; }

bool TFE_ExecutorIsAsync(TFE_Executor* executor) {