code: def amplitude_to_DB(
      x: Tensor,
      multiplier: float,
      amin: float,
      db_multiplier: float,
      top_db: Optional[float] = None,
  ) -> Tensor:
      r"""Turn a spectrogram from the power/amplitude scale to the decibel scale.
  
      .. devices:: CPU CUDA
  
      .. properties:: Autograd TorchScript
  
      The output of each tensor in a batch depends on the maximum value of that tensor,
      and so may return different values for an audio clip split into snippets vs. a full clip.
  
      Args:
  
          x (Tensor): Input spectrogram(s) before being converted to decibel scale.
              The expected shapes are ``(freq, time)``, ``(channel, freq, time)`` or
              ``(..., batch, channel, freq, time)``.
  
              .. note::
  
                 When ``top_db`` is specified, cut-off values are computed for each audio
                 in the batch. Therefore if the input shape is 4D (or larger), different
                 cut-off values are used for audio data in the batch.
                 If the input shape is 2D or 3D, a single cutoff value is used.
  
          multiplier (float): Use 10. for power and 20. for amplitude
          amin (float): Number to clamp ``x``
          db_multiplier (float): Log10(max(reference value and amin))
          top_db (float or None, optional): Minimum negative cut-off in decibels. A reasonable number
              is 80. (Default: ``None``)
  
      Returns:
          Tensor: Output tensor in decibel scale
      """
      x_db = multiplier * torch.log10(torch.clamp(x, min=amin))
      x_db -= multiplier * db_multiplier
  
      if top_db is not None:
          # Expand batch
          shape = x_db.size()
          packed_channels = shape[-3] if x_db.dim() > 2 else 1
          x_db = x_db.reshape(-1, packed_channels, shape[-2], shape[-1])
  
          x_db = torch.max(
              x_db,
              (x_db.amax(dim=(-3, -2, -1)) - top_db).view(-1, 1, 1, 1),
          )
  
          # Repack batch
          x_db = x_db.reshape(shape)
  
      return x_db
  
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 6 for calling a global function with example usage: amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db) and returns: x_db