code: def _apply_convolve_mode(
      conv_result: torch.Tensor, x_length: int, y_length: int, mode: str
  ) -> torch.Tensor:
      valid_convolve_modes = ["full", "valid", "same"]
      if mode == "full":
          return conv_result
      elif mode == "valid":
          target_length = (
              max(x_length, y_length) - min(x_length, y_length) + 1
          )
          start_idx = (conv_result.size(-1) - target_length) // 2
          return conv_result[..., start_idx : start_idx + target_length]
      elif mode == "same":
          start_idx = (conv_result.size(-1) - x_length) // 2
          return conv_result[..., start_idx : start_idx + x_length]
      else:
          raise ValueError(
              f"Unrecognized mode value '{mode}'. Please specify one of {valid_convolve_modes}."
          )
  
imports: import math
  import tempfile
  import warnings
  from collections.abc import Sequence
  from typing import List, Optional, Tuple, Union
  import torch
  import torchaudio
  from torch import Tensor
  from torchaudio._internal.module_utils import deprecated
  from .filtering import highpass_biquad, treble_biquad
label_desc: Use image node_id 49 for calling a global function with example usage: _apply_convolve_mode(conv_result, x_length, y_length, mode) and returns: conv_result, conv_result, conv_result