given:
        buildFile << """
            abstract class TaskWithResultInput extends DefaultTask {

                @Input
                abstract ListProperty<${inputType}> getInput()

                @OutputFile
                abstract RegularFileProperty getOutputFile()

                @TaskAction void action() {
                    println(input.get())
                }

    // If no padding is negative return the input as is.
    if (llvm::all_of(explicit_padding, [](int64_t pad) { return pad >= 0; })) {
      return value;
    }

    auto input_type = mlir::cast<RankedTensorType>(value.getType());
    auto input_shape = input_type.getShape();

    llvm::SmallVector<int64_t, 4> start;
    llvm::SmallVector<int64_t, 4> size;
    start.reserve(explicit_padding.size() / 2);

                                   *index);
  }
  return absl::OkStatus();
}

// Returns the data type of the destination of an edge.
DataType EdgeType(const Edge* edge) {
  return edge->dst()->input_type(edge->dst_input());
}

// Adds the control inputs of `node` to `*deps`.
void AddControlInputs(const Node& node, absl::flat_hash_set<Node*>* deps) {
  for (const Edge* edge : node.in_edges()) {

	1, // 1: istio.networking.nds.v1.NameTable.TableEntry.value:type_name -> istio.networking.nds.v1.NameTable.NameInfo
	2, // [2:2] is the sub-list for method output_type
	2, // [2:2] is the sub-list for method input_type
	2, // [2:2] is the sub-list for extension type_name
	2, // [2:2] is the sub-list for extension extendee
	0, // [0:2] is the sub-list for field type_name
}

func init() { file_dns_proto_nds_proto_init() }

  MemoryTypeVector input_mtypes, output_mtypes;
  DeviceType device_type("");
  TF_RETURN_IF_ERROR(
      DeviceNameToDeviceType(n->assigned_device_name(), &device_type));
  TF_RETURN_IF_ERROR(MemoryTypesForNode(root.graph()->op_registry(),
                                        device_type, n->def(), &input_mtypes,
                                        &output_mtypes));

  } else {
    // Graphdef import path.
    module = tensorflow::LoadFromGraphdefOrMlirSource(
        input_file_name, input_mlir, use_splatted_constant, custom_opdefs,
        specs, debug_info_file, input_arrays, input_dtypes, input_shapes,
        output_arrays, control_output_arrays, &source_mgr, &context);
  }

  // If errors occur, the library call in the above already logged the error
  // message. So we can just return here.

                        const bool enable_per_channel_quantized_weight) {
  const GemmStyleOp gemm_style_op =
      *entry_func_op.getOps<GemmStyleOp>().begin();

  const Type input_type = entry_func_op.getArgumentTypes()[0];
  const Type filter_type = entry_func_op.getArgumentTypes()[1];
  const Type func_result_type = entry_func_op.getResultTypes()[0];

  OpBuilder builder(module);

  std::vector<Type> input_types = GetValueTypes(inputs);
  std::vector<Type> output_types = GetValueTypes(outputs);
  builder.setInsertionPointToEnd(&module.getBodyRegion().back());
  func::FuncOp func_op = builder.create<func::FuncOp>(
      module.getLoc(), name,
      builder.getFunctionType(input_types, output_types));
  func_op.setPrivate();

  return func_op;
}

  }

  bool OpProducesOrConsumesVariant(const Node& node) const {
    auto is_variant = [](DataType dtype) { return dtype == DT_VARIANT; };
    return absl::c_any_of(node.input_types(), is_variant) ||
           absl::c_any_of(node.output_types(), is_variant);
  }

  bool HasXLAKernel(const Node& node,
                    string* uncompilable_reason = nullptr) const;

    control_nodes.try_emplace(from);
    control_nodes[to].incoming.insert(from);
  }

  llvm::SmallVector<mlir::Type, 2> ret_types;
  llvm::SmallVector<mlir::Type, 4> input_types;

  auto func_loc = mlir::NameLoc::get(builder.getStringAttr(name), base_loc);
  std::vector<int> func_inputs = subgraph.inputs;
  if (is_entry_point && !ordered_input_arrays.empty()) {