for k, v := range map[string][]string{
		"table.name":         {"table", "name"},
		"`table`.`name`":     {"table", "name"},
		"'table'.'name'":     {"table", "name"},
		"'table'.name":       {"table", "name"},
		"table1.name_23":     {"table1", "name_23"},
		"`table_1`.`name23`": {"table_1", "name23"},
		"'table23'.'name_1'": {"table23", "name_1"},
		"'table23'.name1":    {"table23", "name1"},
		"'name1'":            {"", "name1"},

	if err != nil {
		log.Fatal(err)
	}
}

type Data struct {
	a, b, c, d string
	Shift1     []int
	Shift2     []int
	Shift3     []int
	Shift4     []int
	Table1     []uint32
	Table2     []uint32
	Table3     []uint32
	Table4     []uint32
}

var funcs = template.FuncMap{
	"dup":     dup,
	"relabel": relabel,
	"rotate":  rotate,
	"idx":     idx,
	"seq":     seq,
}

   * <p>The returned table will be serializable if the specified table is serializable.
   *
   * @param table the table to be wrapped in a synchronized view
   * @return a synchronized view of the specified table
   * @since 22.0
   */
  public static <R extends @Nullable Object, C extends @Nullable Object, V extends @Nullable Object>
      Table<R, C, V> synchronizedTable(Table<R, C, V> table) {
    return Synchronized.table(table, null);
  }

     %3 = "tf.ReadVariableOp"(%arg3) {device = ""} : (tensor<*x!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>
     "tf.LoadTPUEmbeddingAdagradParameters"(%2, %3) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table2"} : (tensor<8xf32>, tensor<8xf32>) -> ()

   * <p>The returned table will be serializable if the specified table is serializable.
   *
   * @param table the table to be wrapped in a synchronized view
   * @return a synchronized view of the specified table
   * @since 22.0
   */
  public static <R extends @Nullable Object, C extends @Nullable Object, V extends @Nullable Object>
      Table<R, C, V> synchronizedTable(Table<R, C, V> table) {
    return Synchronized.table(table, null);
  }

    %control_11 = tf_executor.island wraps "tf.LoadTPUEmbeddingAdagradParameters"(%outputs_2, %outputs_5) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table4"} : (tensor<8xf32>, tensor<8xf32>) -> ()
    tf_executor.fetch
  }
  return
}
// CHECK-LABEL: func @tpu_load_embedding_ops_sink_controls

    return Collector.of(
        tableSupplier,
        (table, input) ->
            mergeTables(
                table,
                rowFunction.apply(input),
                columnFunction.apply(input),
                valueFunction.apply(input),
                mergeFunction),
        (table1, table2) -> {
          for (Table.Cell<R, C, V> cell2 : table2.cellSet()) {
            mergeTables(

     %3 = "tf.ReadVariableOp"(%arg3) {device = ""} : (tensor<*x!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>
     "tf.LoadTPUEmbeddingAdagradParameters"(%2, %3) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table2"} : (tensor<8xf32>, tensor<8xf32>) -> ()

  /**
   * Copies all mappings from the specified table to this table. The effect is equivalent to calling
   * {@link #put} with each row key / column key / value mapping in {@code table}.
   *
   * @param table the table to add to this table
   */
  void putAll(Table<? extends R, ? extends C, ? extends V> table);

  /**
   * Removes the mapping, if any, associated with the given keys.
   *

    return Collector.of(
        tableSupplier,
        (table, input) ->
            mergeTables(
                table,
                rowFunction.apply(input),
                columnFunction.apply(input),
                valueFunction.apply(input),
                mergeFunction),
        (table1, table2) -> {
          for (Table.Cell<R, C, V> cell2 : table2.cellSet()) {
            mergeTables(