code: def _transform_to_feature(
      audio: torch.Tensor,
      sampling_rate_in_hz: int,
      audio_feature_dict: Dict[str, Union[float, int, str]],
      feature_dim: int,
      max_length: int,
      padding_value: float,
      normalization_type: Optional[str] = None,
      type_key: str = TYPE,
  ):
      feature_type: str = str(audio_feature_dict[type_key])
      if feature_type == "raw":
          audio_feature = torch.unsqueeze(audio[0], dim=-1)
      elif feature_type in [
          "stft",
          "stft_phase",
          "group_delay",
          "fbank",
      ]:
          audio_feature = AudioFeatureMixin._get_2D_feature(
              audio,
              feature_type,
              audio_feature_dict,
              sampling_rate_in_hz,
          )
          audio_feature = torch.transpose(audio_feature, 0, 1)
      else:
          raise ValueError(f"{feature_type} is not recognized.")
  
      # Outer conditional is type refinement from Union[str, None] to str
      if normalization_type is not None:
          if normalization_type == "per_file":
              mean = torch.mean(audio_feature, dim=0)
              std = torch.std(audio_feature, dim=0)
              audio_feature = torch.divide(
                  (audio_feature - mean), std + 1.0e-10
              )
          elif normalization_type == "global":
              raise ValueError("not implemented yet")
  
      feature_length = audio_feature.shape[0]
      broadcast_feature_length = min(feature_length, max_length)
      audio_feature_padded = torch.full(
          (max_length, feature_dim),
          padding_value,
          dtype=torch.float32,
          device=audio_feature.device,
      )
      audio_feature_padded[
          :broadcast_feature_length, :
      ] = audio_feature[:max_length, :]
  
      return audio_feature_padded
  
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 6 for calling the AudioFeatureMixin obj's underlying member method code with example usage: obj._transform_to_feature(audio, sampling_rate_in_hz, audio_feature_dict, feature_dim, max_length, padding_value, normalization_type, type_key) and returns: audio_feature_padded