code: def feature_to_pack_sum_xgbmatrix(xs):
      """Convert an extracted multi-stage feature vector to a xgbmatrx in pack-sum format
      Parameters
      ----------
      xs: np.ndarray
          The feature vector
      Returns
      -------
      dmatrix: xgb.DMatrix
          The DMatrix
      pack_ids: List[int]
          pack ids information
      """
      x_flatten = []
      pack_ids = []
  
      for ct, x in enumerate(xs):
          for row in x:
              x_flatten.append(row)
              pack_ids.append(ct)
  
      return xgb.DMatrix(np.array(x_flatten)), pack_ids
  
imports: import multiprocessing
  import logging
  from typing import Dict
  from collections import defaultdict
  import numpy
  from tvm.autotvm.tuner.metric import max_curve
  from .cost_model import PythonBasedModel
  from ..feature import get_per_store_features_from_measure_pairs, get_per_store_features_from_states
  from ..measure_record import RecordReader
label_desc: Use image node_id 1 for calling a global function with example usage: feature_to_pack_sum_xgbmatrix(xs) and returns: xgb,  pack_ids