for (Value v : inputs) operand_types.emplace_back(v.getType());
  llvm::SmallVector<Type, 4> output_types;
  output_types.reserve(outputs.size());
  for (Value v : outputs) output_types.emplace_back(v.getType());

  auto func_type = builder->getFunctionType(operand_types, output_types);

  FuncOp outlined_func =
      FuncOp::create(ops.front()->getLoc(), kHostFunctionAttr, func_type);

      }
      output_types = {new_output_type};
    } else {
      output_types = {output_type.clone(elem_type.getStorageType())};
    }

    SmallVector<Value> args = {q_op.getArg(), scale, zero_point};
    FlatSymbolRefAttr func_name =
        FlatSymbolRefAttr::get(rewriter.getStringAttr(kQuantizeFuncName));

    auto quantize_call = rewriter.create<TF::PartitionedCallOp>(
        loc, output_types, args, func_name,

  ) {
    %1 = "tf.ReduceDataset"(%arg0, %arg1) {
      Targuments = [],
      Tstate = [i64], device = "",
      f = @__reduce_func_1, f._tf_data_function = true,
      output_shapes = [#tf_type.shape<>],
      output_types = [i64], use_inter_op_parallelism = true, _xla_compile_device_type="TPU"} :
 (tensor<!tf_type.variant>, tensor<i64>) -> (tensor<i64>)
    func.return
 }
 ```

 with the following reduction function:

    Variadic<TF_Tensor>:$init_func_other_args,
    Variadic<TF_Tensor>:$next_func_other_args,
    Variadic<TF_Tensor>:$finalize_func_other_args,

    ConfinedAttr<TypeArrayAttr, [ArrayMinCount<1>]>:$output_types,
    ConfinedAttr<TF_ShapeAttrArray, [ArrayMinCount<1>]>:$output_shapes,
    DefaultValuedOptionalAttr<StrAttr, "\"\"">:$metadata
  );

  let results = (outs
    TF_VariantTensor:$handle
  );

  return kTfLiteOk;
}

TfLiteStatus QuantizeModel(ModelT* model, const TensorType& input_type,
                           const TensorType& output_type, bool allow_float,
                           std::string& output_buffer) {
  return QuantizeModel(model, input_type, output_type, allow_float,
                       /*operator_names=*/{}, TensorType_INT8, output_buffer);
}

    auto fft_len_type = mlir::dyn_cast_or_null<ShapedType>(fft_len.getType());
    if (!fft_len_type) return failure();

    auto output_type =
        mlir::dyn_cast_or_null<RankedTensorType>(rfft_op.getResult().getType());
    if (!output_type) return failure();

    // Expanded inputs.
    // Insert at -2 location.
    auto one_ele_type =

  if (inserted) {
    NodeDef ret_def;
    NodeDefBuilder builder(
        absl::StrCat(src_node->name(), "_", src_slot, "_retval"), kRetValOp,
        NodeDebugInfo(src_node->def()));
    DataType dtype = src_node->output_type(src_slot);
    builder.Attr("T", dtype);
    builder.Attr("index", ret_index);
    builder.Input(src_image->name(), src_slot, dtype);
    Status s = builder.Finalize(&ret_def);
    if (!s.ok()) return s;

                             Predicate** predicate) {
    TensorId tensor_id(node->name(), output_idx);

    bool is_boolean_tensor =
        BaseType(node->output_type(tensor_id.index())) == DT_BOOL;
    TF_RET_CHECK(!must_be_true || is_boolean_tensor);

    if (node->type_string() == "Const" && must_be_true) {
      const TensorProto* proto = nullptr;

    OpBuilder builder,
    const std::unique_ptr<tflite::FlatBufferModel>& model_ptr) {
  llvm::SmallVector<Value, 4> operands;
  llvm::SmallVector<mlir::Type, 2> outputTypes;

  const tflite::OperatorCodeT& op_code = *op_codes.at(op.opcode_index);

  const std::string op_name = GetMlirOpName(op, op_code);

  OperationState op_state(loc, op_name);

	15, // 19: istio.workload.GatewayAddress.address:type_name -> istio.workload.NetworkAddress
	11, // 20: istio.workload.Workload.ServicesEntry.value:type_name -> istio.workload.PortList
	21, // [21:21] is the sub-list for method output_type
	21, // [21:21] is the sub-list for method input_type
	21, // [21:21] is the sub-list for extension type_name
	21, // [21:21] is the sub-list for extension extendee
	0,  // [0:21] is the sub-list for field type_name