code: def __init__(
      self,
      hubert_model_path,
      layer,
      sample_rate=16000,
      max_chunk=1600000,
      use_gpu=True,
  ):
      self.sample_rate = sample_rate
  
      self.device = (
          "cuda" if use_gpu and torch.cuda.is_available() else "cpu"
      )
      from espnet2.tasks.hubert import HubertTask
  
      (
          hubert_model,
          hubert_train_args,
      ) = HubertTask.build_model_from_file(
          None,
          hubert_model_path,
          self.device,
      )
      self.model = hubert_model.encoder.hubert_pretrain_model.to(
          self.device
      ).eval()
  
      self.layer = layer
      self.max_chunk = max_chunk
      logger.info(f" max_chunk = {self.max_chunk}")
  
imports: import json
  import logging
  import os
  import re
  import sys
  from typing import List, Optional, Tuple, Union
  import numpy
  import soundfile
  import torch
  import torchaudio
  from espnet2.asr.frontend.s3prl import S3prlFrontend
  from espnet2.iterators.sequence_iter_factory import SequenceIterFactory
  from espnet2.samplers.num_elements_batch_sampler import NumElementsBatchSampler
  from espnet2.train.collate_fn import CommonCollateFn
  from espnet2.train.dataset import ESPnetDataset
  from espnet.utils.cli_writers import file_writer_helper
label_desc: Use image node_id 1 to create a new ESPnetHubertFeatureReader object from inherited base classes: BaseFeatureReader with example: obj = ESPnetHubertFeatureReader(hubert_model_path, layer, sample_rate, max_chunk, use_gpu)