code: def _load_feature(
      feature_dict: Dict[str, Tensor],
      feature_name: Optional[str],
      manifest_entry: Dict[str, Any],
      audio_dir: Path,
      feature_dir: Path,
      indices: Optional[Tuple[int, int]] = None,
  ) -> None:
      """
      If feature_name is provided, load feature into feature_dict from .npy file.
      """
      if feature_name is None:
          return
  
      feature_filepath = _get_feature_filepath(
          manifest_entry=manifest_entry,
          audio_dir=audio_dir,
          feature_dir=feature_dir,
          feature_name=feature_name,
      )
      feature_filepath = str(feature_filepath)
      if indices:
          feature_mmap = np.load(feature_filepath, mmap_mode="r")
          feature_array = feature_mmap[indices[0] : indices[1]]
          feature_array = np.copy(feature_array)
      else:
          feature_array = np.load(feature_filepath)
      feature_tensor = torch.from_numpy(feature_array)
      feature_dict[feature_name] = feature_tensor
  
imports: import math
  from abc import ABC, abstractmethod
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Tuple, Union
  import librosa
  import numpy
  import torch
  from torch import Tensor
  from nemo.collections.asr.modules import AudioToMelSpectrogramPreprocessor
  from nemo.collections.tts.parts.utils.tts_dataset_utils import get_audio_filepaths, normalize_volume, stack_tensors
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 4 for calling a global function with example usage: _load_feature(feature_dict, feature_name, manifest_entry, audio_dir, feature_dir, indices) without return types