code: def _process_in_memory(
      column,
      audio_feature_dict,
      feature_dim,
      max_length,
      padding_value,
      normalization_type,
      audio_file_length_limit_in_s,
      backend,
  ):
      df_engine = backend.df_engine
      if _TORCH_200:
          # Read audio from path if the version of torch is >= 2.0.0.
          raw_audio = backend.read_binary_files(
              column, map_fn=read_audio_from_path
          )
      else:
          raw_audio = backend.read_binary_files(
              column, map_fn=read_audio_from_bytes_obj
          )
  
      try:
          default_audio = get_default_audio(
              [
                  audio
                  for audio in raw_audio
                  if is_torch_audio_tuple(audio)
              ]
          )
      except RuntimeError as e:
          raise RuntimeError(
              f"Unable to process audio files provided: {e}"
          ) from e
  
      raw_audio = df_engine.map_objects(
          raw_audio,
          lambda row: row
          if is_torch_audio_tuple(row)
          else default_audio,
      )
      processed_audio = df_engine.map_objects(
          raw_audio,
          lambda row: AudioFeatureMixin._transform_to_feature(
              audio=row[0],
              sampling_rate_in_hz=row[1],
              audio_feature_dict=audio_feature_dict,
              feature_dim=feature_dim,
              max_length=max_length,
              padding_value=padding_value,
              normalization_type=normalization_type,
          ).numpy(),  # non-torchscript preprocessing requires np.ndarray
      )
  
      audio_stats = df_engine.map_objects(
          raw_audio,
          lambda row: AudioFeatureMixin._get_stats(
              audio=row[0],
              sampling_rate_in_hz=row[1],
              max_length_in_s=audio_file_length_limit_in_s,
          ),
      )
  
      def reduce(series):
          merged_stats = None
          for audio_stats in series:
              if merged_stats is None:
                  merged_stats = audio_stats.copy()
              else:
                  AudioFeatureMixin._merge_stats(
                      merged_stats, audio_stats
                  )
          return merged_stats
  
      merged_stats = df_engine.reduce_objects(audio_stats, reduce)
      merged_stats["mean"] = calculate_mean(
          merged_stats["sum"], merged_stats["count"]
      )
      merged_stats["var"] = calculate_var(
          merged_stats["sum"],
          merged_stats["sum2"],
          merged_stats["count"],
      )
      merged_stats["std"] = np.sqrt(
          merged_stats["var"] / float(merged_stats["count"])
      )
      print_statistics = (
          "{} audio files loaded.\n"
          "Statistics of audio file lengths:\n"
          "- mean: {:.4f}\n"
          "- std: {:.4f}\n"
          "- max: {:.4f}\n"
          "- min: {:.4f}\n"
          "- cropped audio_files: {}\n"
          "Max length was given as {}s"
      ).format(
          merged_stats["count"],
          merged_stats["mean"],
          merged_stats["std"],
          merged_stats["max"],
          merged_stats["min"],
          merged_stats["cropped"],
          audio_file_length_limit_in_s,
      )
      logger.debug(print_statistics)
      return processed_audio
  
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 5 for calling the AudioFeatureMixin obj's underlying member method code with example usage: obj._process_in_memory(column, audio_feature_dict, feature_dim, max_length, padding_value, normalization_type, audio_file_length_limit_in_s, backend) and returns: processed_audio, merged_stats