// This includes functions used for device registration and interfaces needed
// for testing.
#ifndef TENSORFLOW_C_EXPERIMENTAL_STREAM_EXECUTOR_STREAM_EXECUTOR_INTERNAL_H_
#define TENSORFLOW_C_EXPERIMENTAL_STREAM_EXECUTOR_STREAM_EXECUTOR_INTERNAL_H_

#include "tensorflow/c/experimental/stream_executor/stream_executor.h"
#include "tensorflow/c/tf_status_helper.h"

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_COMPILER_MLIR_LITE_PYTHON_SAVED_MODEL_TO_TFL_FLATBUFFER_H_
#define TENSORFLOW_COMPILER_MLIR_LITE_PYTHON_SAVED_MODEL_TO_TFL_FLATBUFFER_H_

#include "tensorflow/compiler/mlir/quantization/tensorflow/python/py_function_lib.h"
#include "tensorflow/core/framework/graph.pb.h"

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

plugins {
    id("gradlebuild.distribution.api-java")
}

description = "Declarations to define JVM toolchains shared between launcher and daemon"

dependencies {
    api(libs.inject)
    api(libs.jsr305)

    api(project(":stdlib-java-extensions"))
    api(project(":base-services"))

//   2. How each block dimension in (dn, ..., dn+k-1) maps to the original
//      tensor dimension in (d0, ..., dn-1).
//   3. In the traversal order defined above, the format (dense vs. sparse) and
//      index metadata for each dimension. For a dense dimension, this is just
//      the size of that dimension. For a sparse dimension, it's the same as
//      the compressed index defined in the Compressed Sparse Row (CSR) format.

             .getOps<TF::CalibrationStatisticsSaverOp>()
             .empty()) {
      return true;
    }
  }
  return false;
}

}  // namespace

#define GEN_PASS_DECL_INSERTCALIBRATIONSTATISTICSSAVERPASS
#define GEN_PASS_DEF_INSERTCALIBRATIONSTATISTICSSAVERPASS
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.h.inc"

class InsertCalibrationStatisticsSaverPass

limitations under the License.
==============================================================================*/

#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

        ensureOpened();

        if (className == null || protectionDomain == null) {
            // JVM allows to define a class with "null" name through Unsafe. LambdaMetafactory in Java 8 defines a SAM implementation for method handle this way.
            // ProtectionDomain is unlikely to be null in practice, but checking it doesn't hurt.

    ],
)

gentbl_cc_library(
    name = "mlrt_ops_inc_gen",
    tbl_outs = [
        (
            ["-gen-op-decls"],
            "mlrt_ops.h.inc",
        ),
        (
            ["-gen-op-defs"],
            "mlrt_ops.cpp.inc",
        ),
    ],
    tblgen = "@llvm-project//mlir:mlir-tblgen",
    td_file = "mlrt_ops.td",
    deps = [":mlrt_td_files"],
)

cc_library(
    name = "mlrt_ops",

	htmlRouter := router.NewRoute().PathPrefix("/" + t.Prefix() + "z").Subrouter()
	jsonRouter := router.NewRoute().PathPrefix("/" + t.Prefix() + "j").Subrouter()

	tmpl := template.Must(template.Must(layout.Clone()).Parse("{{ define \"title\" }}" + t.Title() + "{{ end }}"))
	t.Activate(fw.NewContext(htmlRouter, jsonRouter, tmpl))
}

// getLocalIP returns a non loopback local IP of the host
func getLocalIP() string {

limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_COMPILER_MLIR_TF2XLA_TRANSFORMS_LEGALIZE_TF_WITH_TF2XLA_PASSES_H_
#define TENSORFLOW_COMPILER_MLIR_TF2XLA_TRANSFORMS_LEGALIZE_TF_WITH_TF2XLA_PASSES_H_

#include <memory>
#include <optional>

#include "llvm/ADT/StringRef.h"
#include "mlir/IR/MLIRContext.h"  // from @llvm-project