parameters: (func, is_multi_dimentional)
return_statement: return f
docstring: Builds a specialized version of the function.
docstring_summary: Builds a specialized version of the function.
docstring_tokens: Builds a specialized version of the function .
function: def _specialize_make_split_dense(func, is_multi_dimentional):
    """Builds a specialized version of the function."""
  
    @function.Defun(
        dtypes.resource,
        dtypes.resource,
        dtypes.int64,
        dtypes.int64,
        dtypes.int32,
        dtypes.int32,
        dtypes.int32,
        dtypes.float32,
        dtypes.float32,
        dtypes.float32,
        dtypes.float32,
        dtypes.bool,
        dtypes.int32,
        noinline=True)
    def f(quantile_accumulator_handle, stats_accumulator_handle, stamp_token,
          next_stamp_token, multiclass_strategy, class_id, feature_column_id,
          l1_regularization, l2_regularization, tree_complexity_regularization,
          min_node_weight, loss_uses_sum_reduction, weak_learner_type):
      """Function that builds splits for a sparse feature column."""
      return func(quantile_accumulator_handle, stats_accumulator_handle,
                  stamp_token, next_stamp_token, multiclass_strategy, class_id,
                  feature_column_id, l1_regularization, l2_regularization,
                  tree_complexity_regularization, min_node_weight,
                  is_multi_dimentional, loss_uses_sum_reduction,
                  weak_learner_type)
  
    return f
function_tokens: def _specialize_make_split_dense ( func , is_multi_dimentional ) : @ function . Defun ( dtypes . resource , dtypes . resource , dtypes . int64 , dtypes . int64 , dtypes . int32 , dtypes . int32 , dtypes . int32 , dtypes . float32 , dtypes . float32 , dtypes . float32 , dtypes . float32 , dtypes . bool , dtypes . int32 , noinline = True ) def f ( quantile_accumulator_handle , stats_accumulator_handle , stamp_token , next_stamp_token , multiclass_strategy , class_id , feature_column_id , l1_regularization , l2_regularization , tree_complexity_regularization , min_node_weight , loss_uses_sum_reduction , weak_learner_type ) : """Function that builds splits for a sparse feature column.""" return func ( quantile_accumulator_handle , stats_accumulator_handle , stamp_token , next_stamp_token , multiclass_strategy , class_id , feature_column_id , l1_regularization , l2_regularization , tree_complexity_regularization , min_node_weight , is_multi_dimentional , loss_uses_sum_reduction , weak_learner_type ) return f