code: def load_feature_shard(rspecifier, in_filetype, percent):
      feats = []
      for utt, feat in file_reader_helper(rspecifier, in_filetype):
          feats.append(feat)
      if percent < 0:
          return np.concatenate(feats, axis=0)
      else:
          nsample = int(np.ceil(len(feats) * percent))
          sampled_feat = random.sample(feats, nsample)
          sampled_feat = np.concatenate(
              sampled_feat,
              axis=0,
          )
          logger.info(
              (
                  f"sampled {nsample} utterances, {len(sampled_feat)} frames "
                  f"from rspecifier {rspecifier}"
              )
          )
          return sampled_feat
  
file_path: espnet/egs2/iemocap/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 3 for calling a global function with example usage: load_feature_shard(rspecifier, in_filetype, percent) and returns: np, sampled_feat