code: def load_feature(rspecifiers, in_filetype, percent):
      assert percent <= 1.0
      if not isinstance(rspecifiers, list):
          rspecifiers = [rspecifiers]
      feat = np.concatenate(
          [
              load_feature_shard(rspecifier, in_filetype, percent)
              for rspecifier in rspecifiers
          ],
          axis=0,
      )
      logging.info(f"loaded feature with dimension {feat.shape}")
      return feat
  
file_path: espnet/egs2/yesno/asr1/pyscripts/utils/learn_kmeans.py
imports: import argparse
  import logging
  import os
  import random
  import sys
  import joblib
  import numpy
  from sklearn.cluster import MiniBatchKMeans
  from espnet.utils.cli_readers import file_reader_helper
label_desc: Use image node_id 4 for calling a global function with example usage: load_feature(rspecifiers, in_filetype, percent) and returns: feat