code: def load_dependencies(hparams, run_opts):
      """Loads any pre-trained dependencies (e.g. language models)
  
      Arguments
      ---------
      hparams: dict
          the hyperparamters dictionary
      run_opts: dict
          run options
      """
      deps_pretrainer = hparams.get("deps_pretrainer")
      if deps_pretrainer:
          run_on_main(deps_pretrainer.collect_files)
          deps_pretrainer.load_collected()
  
imports: from speechbrain.dataio.dataset import FilteredSortedDynamicItemDataset, DynamicItemDataset
  from speechbrain.dataio.sampler import BalancingDataSampler
  from speechbrain.utils.data_utils import undo_padding
  import datasets
  import logging
  import os
  import random
  import speechbrain
  import sys
  from enum import Enum
  from collections import namedtuple
  from hyperpyyaml import load_hyperpyyaml
  from functools import partial
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.utils.pretrained import save_for_pretrained
  from speechbrain.lobes.models.g2p.dataio import enable_eos_bos, grapheme_pipeline, phoneme_pipeline, tokenizer_encode_pipeline, add_bos_eos, get_sequence_key, phonemes_to_label
  from speechbrain.dataio.wer import print_alignments
  from speechbrain.wordemb.util import expand_to_chars
  from io import StringIO
  from speechbrain.utils import hpopt
  import numpy
label_desc: Use image node_id 7 for calling a global function with example usage: load_dependencies(hparams, run_opts) without return types