code: def add_feature_data(
      feature_config,
      input_df,
      proc_df,
      metadata,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
      skip_save_processed_input,
  ):
      set_default_value(
          feature_config["preprocessing"],
          "in_memory",
          preprocessing_parameters["in_memory"],
      )
  
      name = feature_config[NAME]
      column = input_df[feature_config[COLUMN]]
  
      num_audio_files = len(column)
      if num_audio_files == 0:
          raise ValueError(
              "There are no audio files in the dataset provided."
          )
  
      first_audio_entry = next(iter(column))
      logger.debug(
          f"Detected audio feature type is {type(first_audio_entry)}"
      )
  
      if not isinstance(first_audio_entry, str) and not isinstance(
          first_audio_entry, torch.Tensor
      ):
          raise ValueError(
              "Invalid audio feature data type.  Detected type is {}, "
              "expected either string for local/remote file path or Torch Tensor.".format(
                  type(first_audio_entry)
              )
          )
  
      src_path = None
      if SRC in metadata:
          if isinstance(
              first_audio_entry, str
          ) and not has_remote_protocol(first_audio_entry):
              src_path = os.path.dirname(
                  os.path.abspath(metadata.get(SRC))
              )
      abs_path_column = (
          backend.df_engine.map_objects(  # This gets the CSV file path
              column,
              lambda row: get_abs_path(src_path, row)
              if isinstance(row, str)
              else row,
          )
      )
  
      num_audio_utterances = len(input_df[feature_config[COLUMN]])
      padding_value = preprocessing_parameters["padding_value"]
      normalization_type = preprocessing_parameters["norm"]
  
      feature_dim = metadata[name]["feature_dim"]
      max_length = metadata[name]["max_length"]
      audio_feature_dict = {
          key: value
          for key, value in preprocessing_parameters.items()
          if key in AUDIO_FEATURE_KEYS and value is not None
      }
      audio_file_length_limit_in_s = preprocessing_parameters[
          "audio_file_length_limit_in_s"
      ]
  
      if num_audio_utterances == 0:
          raise ValueError(
              "There are no audio files in the dataset provided."
          )
  
      if feature_config[PREPROCESSING]["in_memory"]:
          audio_features = AudioFeatureMixin._process_in_memory(
              abs_path_column,
              audio_feature_dict,
              feature_dim,
              max_length,
              padding_value,
              normalization_type,
              audio_file_length_limit_in_s,
              backend,
          )
          proc_df[feature_config[PROC_COLUMN]] = audio_features
  
      return proc_df
  
imports: import logging
  import os
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torchaudio
  from packaging import version
  from ludwig.constants import AUDIO, AUDIO_FEATURE_KEYS, COLUMN, NAME, PREPROCESSING, PROC_COLUMN, SRC, TYPE
  from ludwig.features.base_feature import BaseFeatureMixin
  from ludwig.features.sequence_feature import SequenceInputFeature
  from ludwig.schema.features.audio_feature import AudioInputFeatureConfig
  from ludwig.types import FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.audio_utils import calculate_mean, calculate_var, get_default_audio, get_fbank, get_group_delay, get_length_in_samp, get_max_length_stft_based, get_non_symmetric_length, get_phase_stft_magnitude, get_stft_magnitude, is_torch_audio_tuple, read_audio_from_bytes_obj, read_audio_from_path
  from ludwig.utils.data_utils import get_abs_path
  from ludwig.utils.fs_utils import has_remote_protocol
  from ludwig.utils.misc_utils import set_default_value
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 10 for calling the AudioFeatureMixin obj's underlying member method code with example usage: obj.add_feature_data(feature_config, input_df, proc_df, metadata, preprocessing_parameters, backend, skip_save_processed_input) and returns: proc_df