done(status);
    return;
  }
  if (auto st = stream_->BlockHostUntilDone(); !st.ok()) {
    done_event_.SetError(absl::InternalError(absl::StrFormat(
        "failed to synchronize send operation with a stream: %s",
        st.ToString())));
    return;
  }

  done_event_.SetStateConcrete();
  done(absl::OkStatus());
}

  *out_context = device_context;
  return absl::OkStatus();
}

// Warn about XLA_CPU/XLA_GPU exactly once.
static void ShowXlaDeviceDeprecationWarning(
    absl::string_view compilation_device_name) {
  static absl::once_flag once;
  if (absl::StrContains(compilation_device_name, "CPU") ||
      absl::StrContains(compilation_device_name, "GPU")) {
    absl::call_once(once, [] {

#ifndef TENSORFLOW_COMPILER_MLIR_LITE_EXPERIMENTAL_TAC_UTILS_UTILS_H_
#define TENSORFLOW_COMPILER_MLIR_LITE_EXPERIMENTAL_TAC_UTILS_UTILS_H_

#include <string>

#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/MLIRContext.h"  // from @llvm-project

  }

  absl::Status CreateMlirModule(std::string mlir_module_filename) {
    std::string mlir_module_path = TestDataPath() + mlir_module_filename;
    mlir_module_ =
        mlir::parseSourceFile<mlir::ModuleOp>(mlir_module_path, &context_);
    if (!mlir_module_) {
      return absl::Status(
          absl::StatusCode::kNotFound,
          absl::StrCat("Could not find MLIR module at ", mlir_module_path));

#ifndef TENSORFLOW_COMPILER_MLIR_LITE_QUANTIZATION_LITE_QUANTIZE_MODEL_H_
#define TENSORFLOW_COMPILER_MLIR_LITE_QUANTIZATION_LITE_QUANTIZE_MODEL_H_

#include <string>
#include <unordered_set>

#include "absl/container/flat_hash_set.h"
#include "absl/strings/string_view.h"
#include "tensorflow/compiler/mlir/lite/debug/debug_options.pb.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/lite/c/c_api_types.h"

#include "tensorflow/compiler/mlir/tf2xla/api/v2/legalize_tf.h"

#include <memory>
#include <string>
#include <string_view>
#include <variant>
#include <vector>

#include "absl/log/log.h"
#include "absl/status/status.h"
#include "absl/strings/str_cat.h"
#include "absl/types/variant.h"
#include "llvm/ADT/ScopeExit.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.h"

#include "tensorflow/compiler/mlir/tf2xla/api/v1/compile_tf_graph.h"

#include <cstdint>
#include <memory>
#include <string>
#include <variant>
#include <vector>

#include "absl/container/flat_hash_set.h"
#include "absl/log/log.h"
#include "absl/strings/str_cat.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/StringRef.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project

    }
  }

  if (!is_module_updated) {
    VLOG(2) << "MLIR module is not updated. Using the original graph. "
            << "Do not convert mlir module back to graph";
    return absl::OkStatus();
  }
  GraphExportConfig export_config;
  absl::flat_hash_set<Node*> control_ret_nodes;

  timings.Reset({kTfMlirCategory, "convert_mlir_to_graph"});
  // Some or all passes are enabled. Convert MLIR module and return back

#include "tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.h"

#include <cstdint>
#include <optional>

#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "absl/status/status.h"
#include "absl/strings/string_view.h"
#include "llvm/Support/MathExtras.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project

==============================================================================*/

#include "tensorflow/compiler/mlir/tf2xla/api/v2/tf_dialect_to_executor.h"

#include <memory>
#include <string>

#include "absl/log/log.h"
#include "absl/status/status.h"
#include "llvm/ADT/StringRef.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project