code: def _aggregate_score_dicts(scores):
      """Aggregate the list of dict to dict of np ndarray
  
      The aggregated output of _aggregate_score_dicts will be a list of dict
      of form [{'prec': 0.1, 'acc':1.0}, {'prec': 0.1, 'acc':1.0}, ...]
      Convert it to a dict of array {'prec': np.array([0.1 ...]), ...}
  
      Parameters
      ----------
  
      scores : list of dict
          List of dicts of the scores for all scorers. This is a flat list,
          assumed originally to be of row major order.
  
      Example
      -------
  
      >>> scores = [{'a': 1, 'b':10}, {'a': 2, 'b':2}, {'a': 3, 'b':3},
      ...           {'a': 10, 'b': 10}]                         # doctest: +SKIP
      >>> _aggregate_score_dicts(scores)                        # doctest: +SKIP
      {'a': array([1, 2, 3, 10]),
       'b': array([10, 2, 3, 10])}
      """
  
      return {
          key: np.asarray([score[key] for score in scores])
          if isinstance(scores[0][key], numbers.Number)
          else [score[key] for score in scores]
          for key in scores[0]
      }
  
imports: import functools
  import logging
  import numbers
  import time
  import warnings
  from collections import Counter
  from contextlib import suppress
  from itertools import compress
  from traceback import format_exc
  import numpy
  import scipy.sparse
  from catboost import CatBoostClassifier, CatBoostRegressor
  from joblib import Parallel, logger
  from scipy.sparse import issparse
  from sklearn import config_context, get_config
  from sklearn.base import clone, is_classifier
  from sklearn.exceptions import FitFailedWarning, NotFittedError
  from sklearn.metrics import check_scoring
  from sklearn.metrics._scorer import _MultimetricScorer
  from sklearn.model_selection import check_cv
  from sklearn.utils.fixes import np_version, parse_version
  from sklearn.utils.validation import indexable
label_desc: Use image node_id 3 for calling a global function with example usage: _aggregate_score_dicts(scores) and returns: unknown