// Restore variable and resources in the SavedModel export dir for the
// indicated metagraph.
// The recommended way to load a saved model is to call LoadSavedModel,
// which provides an already initialized Metagraph, Session, and DebugInfo.
Status RestoreSession(const RunOptions& run_options,
                      const MetaGraphDef& meta_graph, const string& export_dir,
                      std::unique_ptr<Session>* session);

// RUN: tf-quant-opt %s -mlir-print-debuginfo -mlir-print-local-scope -quant-add-quantization-unit-loc | FileCheck %s

func.func @conv2d_unmatching_loc_pattern(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x3x2x2xf32>) {
  %cst = "tf.Const"() {device = "", value = dense_resource<__elided__> : tensor<2x3x3x2xbf16>} : () -> tensor<2x3x3x2xbf16>
  %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xbf16>

// RUN: tf-quant-opt %s -split-input-file -mlir-print-debuginfo -mlir-print-local-scope -quant-add-quantization-unit-loc -inline -quant-prepare-lifting -quant-lift-quantizable-spots-as-functions='target-opset=XLA' | FileCheck %s

// This file test the selective quantiation feature in TF Quantizer. In the test
// config, the op named "test_opt_out" will not be quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1269 : i32}} {

                            const GraphDebugInfo& debug_info)
      : meta_graph_def_(meta_graph_def), debug_info_(debug_info) {
    DCHECK(meta_graph_def);
  }

  virtual ~SavedModelMLIRImportInput();

  // The original MetaGraphDef of the savedmodel.
  const MetaGraphDef& meta_graph_def() const { return *meta_graph_def_; }

  const GraphDebugInfo& debug_info() const { return debug_info_; }

                      internal::FileExists(Env::Default(), debug_info_pb_path));
  if (debug_info_pb_exists) {
    GraphDebugInfo debug_info;
    TF_RETURN_IF_ERROR(
        ReadBinaryProto(Env::Default(), debug_info_pb_path, &debug_info));
    *debug_info_proto = std::make_unique<GraphDebugInfo>(std::move(debug_info));
  }
  return absl::OkStatus();
}

    srcs = ["duplicate_method_names_v1.py"],
    deps = [
        ":common_v1",
        "//tensorflow:tensorflow_py",
    ],
)

py_strict_binary(
    name = "debug_info",
    srcs = ["debug_info.py"],
    deps = [
        ":common",
        "//tensorflow:tensorflow_py",
    ],
)

py_strict_binary(
    name = "keras",
    srcs = ["keras.py"],
    deps = [

#include "tsl/platform/statusor.h"

namespace tensorflow {

absl::Status ConvertGraphDefToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,
    const GraphDebugInfo& debug_info, const GraphDef& input,
    std::string* result) {
  using ::tflite::optimize::ReducedPrecisionSupport;
  mlir::MLIRContext context;
  GraphImportConfig specs;
  mlir::quant::QuantizationSpecs quant_specs;

    // specifiable.
    GraphDebugInfo debug_info;
    switch (config.dialect) {
      case MlirDumpConfig::Dialect::kTFG: {
        TF_ASSIGN_OR_RETURN(module,
                            mlir::tfg::ImportGraphAndFunctionsToMlir(
                                &context, debug_info, graph,
                                flib_def ? *flib_def : graph.flib_def()));
        break;

// it fails to convert the input.
absl::Status ConvertGraphDefToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,
    const GraphDebugInfo& debug_info, const GraphDef& input,
    std::string* result);

}  // namespace tensorflow

          << pass->name() << ": " << status.message();
      return signalPassFailure();
    }
  }

  // Convert Graph to MLIR
  GraphDebugInfo debug_info;
  GraphImportConfig specs;
  auto module_or_status =
      ConvertGraphToMlir(**options.graph, debug_info, flib_def, specs, &ctx);
  if (!module_or_status.ok()) {
    mlir::emitError(mlir::UnknownLoc::get(&ctx))
        << module_or_status.status().message();