code: def _pre_emphasize_data(
      data: torch.Tensor, emphasize_value: float = 0.97
  ):
      # Increase precision in order to achieve parity with scipy.signal.lfilter implementation
      filter_window = torch.tensor(
          [1.0, -emphasize_value],
          dtype=torch.float64,
          device=data.device,
      )
      a_coeffs = torch.tensor(
          [1, 0], dtype=torch.float64, device=data.device
      )
      pre_emphasized_data = torchaudio.functional.lfilter(
          data.to(dtype=torch.float64),
          a_coeffs,
          filter_window,
          clamp=False,
      ).to(torch.float32)
      return pre_emphasized_data
  
imports: import functools
  import logging
  from io import BytesIO
  from typing import Any, List, Optional, Union
  import torch
  import torch.nn.functional
  import torchaudio
  from packaging import version
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import DEFAULT_AUDIO_TENSOR_LENGTH
  from ludwig.utils.types import TorchAudioTuple
label_desc: Use image node_id 5 for calling a global function with example usage: _pre_emphasize_data(data, emphasize_value) and returns: pre_emphasized_data