parser.parseAttribute(device, "device", result.attributes) ||
       parser.parseRParen()))
    return mlir::failure();

  if (options.has_op_name &&
      parser.parseAttribute(op_name, "op_name", result.attributes))
    return mlir::failure();

  if (options.has_symbol_ref &&
      parser.parseAttribute(f, "f", result.attributes))
    return mlir::failure();

    dialect_name_of_[op_with_dialect_name] = dialect_name;
    op_name_of_[op_with_dialect_name] = op_name;
    ++total_ops_;
  });
  PrintSummary();
}

void PrintOpStatsPass::CountOp(
    DenseMap<StringRef, llvm::StringMap<int64_t>> &op_count_map,
    StringRef op_name, StringRef dtype) {
  auto &op_counts = op_count_map[op_name];
  if (auto it = op_counts.find(dtype); it != op_counts.end()) {
    it->second++;
  } else {

        "mlir_second_phase_failed_legalization_op_count",
        "Counts which op fails to legalize", "op_name");

auto* mlir_non_static_op_count = tensorflow::monitoring::Counter<1>::New(
    "/tensorflow/core/tf2xla/"
    "mlir_second_phase_non_static_op_count",
    "Counts which ops do not have static results", "op_name");

auto* mlir_non_static_op_skip_count = tensorflow::monitoring::Counter<1>::New(
    "/tensorflow/core/tf2xla/"

      }
    }

    return success();
  }

 private:
  // Parses TFLite Flex op's `custom_options` and returns the TF
  // `op_name` and TF dialect MLIR op attributes.
  static LogicalResult ParseCustomOption(
      StringRef custom_options, Location loc, std::string& op_name,
      SmallVectorImpl<NamedAttribute>& attributes,
      tensorflow::NodeDef& node_def) {

    num_args: The number of inputs.
    device: The tensorflow device. eg. "/CPU:0"
    op_attrs: The tensorflow attributes excluding the func attrs.
    op_func_attrs: The func attrs.
    op_name: The tensorflow op name. eg. "tf.AddV2"

    Example:
      %out_ch = tfrt_fallback_async.createop(%in_ch) key(0) device("/CPU:0")

    auto custom_op = llvm::dyn_cast_or_null<CustomOp>(op);
    if (!custom_op) return false;

    // Custom op which is marked in the custom op map is quantizable.
    std::string op_name = custom_op.getCustomCode().str();
    return (custom_op_map.find(op_name) == custom_op_map.end()) ? false : true;
  }

  static bool AllowDynamicRangeQuantizedOperand(
      Operation* quantized_op, const quant::CustomOpMap& custom_op_map) {

  OpBuilder builder(func);

  func.walk([&](Operation *op) {
    if (op->hasTrait<OpTrait::IsTerminator>()) return;
    auto op_name = op_to_name_(op);

    // Check the named info collection first.
    auto it = name_to_info_.find(op_name);
    if (it != name_to_info_.end()) {
      ImportAsStatsOps(builder, op, it->second.first, it->second.second);
      return;
    }

    public companion object {
        public val EP_NAME: ExtensionPointName<KaResolveExtensionProvider> =
            ExtensionPointName<KaResolveExtensionProvider>("org.jetbrains.kotlin.kaResolveExtensionProvider")

        public fun provideExtensionsFor(module: KtModule): List<KaResolveExtension> {
            return EP_NAME.getExtensionList(module.project).flatMap { it.provideExtensionsFor(module) }
        }
    }

        op_state.operands[1] = shape_op;
      }
    }
  }
  if (op_name == "vhlo.reduce_v1" || op_name == "vhlo.reduce_window_v1" ||
      op_name == "vhlo.sort_v1" || op_name == "vhlo.scatter_v1") {
    op_state.addRegion();
  }
  if (op_name == "vhlo.while_v1") {
    op_state.addRegion();
    op_state.addRegion();
  }

    public companion object {
        public val EP_NAME: ExtensionPointName<AdditionalKDocResolutionProvider> =
            ExtensionPointName<AdditionalKDocResolutionProvider>("org.jetbrains.kotlin.analysis.additionalKDocResolutionProvider")

        public fun resolveKdocFqName(analysisSession: KaSession, fqName: FqName, contextElement: KtElement): Collection<KaSymbol> =