code: def load_audio(manifest_path, max_keep, min_keep):
      n_long, n_short = 0, 0
      names, inds, sizes = [], [], []
      with open(manifest_path) as f:
          root = f.readline().strip()
          for ind, line in enumerate(f):
              items = line.strip().split("\t")
              assert len(items) == 2, line
              sz = int(items[1])
              if min_keep is not None and sz < min_keep:
                  n_short += 1
              elif max_keep is not None and sz > max_keep:
                  n_long += 1
              else:
                  names.append(items[0])
                  inds.append(ind)
                  sizes.append(sz)
      tot = ind + 1
      logger.info(
          (
              f"max_keep={max_keep}, min_keep={min_keep}, "
              f"loaded {len(names)}, skipped {n_short} short and {n_long} long, "
              f"longest-loaded={max(sizes)}, shortest-loaded={min(sizes)}"
          )
      )
      return root, names, inds, tot, sizes
  
imports: import itertools
  import logging
  import os
  import sys
  from typing import Any, List, Optional, Union
  import numpy
  import torch
  import torch.nn.functional
  from fairseq.data import data_utils
  from fairseq.data.fairseq_dataset import FairseqDataset
  from fairseq.data.audio.audio_utils import parse_path, read_from_stored_zip
  import io
label_desc: Use image node_id 1 for calling a global function with example usage: load_audio(manifest_path, max_keep, min_keep) and returns: root,  names,  inds,  tot,  sizes