class_docstr: Constructs a CLAP feature extractor.
  
  This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
  most of the main methods. Users should refer to this superclass for more information regarding those methods.
  
  This class extracts mel-filter bank features from raw speech using a custom numpy implementation of the *Short Time
  Fourier Transform* (STFT) which should match pytorch's `torch.stft` equivalent.
  
  Args:
      feature_size (`int`, *optional*, defaults to 64):
          The feature dimension of the extracted Mel spectrograms. This corresponds to the number of mel filters
          (`n_mels`).
      sampling_rate (`int`, *optional*, defaults to 48000):
          The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). This only serves
          to warn users if the audio fed to the feature extractor does not have the same sampling rate.
      hop_length (`int`,*optional*, defaults to 480):
          Length of the overlaping windows for the STFT used to obtain the Mel Spectrogram. The audio will be split
          in smaller `frames` with a step of `hop_length` between each frame.
      max_length_s (`int`, *optional*, defaults to 10):
          The maximum input length of the model in seconds. This is used to pad the audio.
      fft_window_size (`int`, *optional*, defaults to 1024):
          Size of the window (in samples) on which the Fourier transform is applied. This controls the frequency
          resolution of the spectrogram. 400 means that the fourrier transform is computed on windows of 400 samples.
      padding_value (`float`, *optional*, defaults to 0.0):
          Padding value used to pad the audio. Should correspond to silences.
      return_attention_mask (`bool`, *optional*, defaults to `False`):
          Whether or not the model should return the attention masks coresponding to the input.
      frequency_min (`float`, *optional*, defaults to 0):
          The lowest frequency of interest. The STFT will not be computed for values below this.
      frequency_max (`float`, *optional*, defaults to 14000):
          The highest frequency of interest. The STFT will not be computed for values above this.
      top_db (`float`, *optional*):
          The highest decibel value used to convert the mel spectrogram to the log scale. For more details see the
          `audio_utils.power_to_db` function
      truncation (`str`, *optional*, defaults to `"fusion"`):
          Truncation pattern for long audio inputs. Two patterns are available:
              - `fusion` will use `_random_mel_fusion`, which stacks 3 random crops from the mel spectrogram and a
                downsampled version of the entire mel spectrogram.
          If `config.fusion` is set to True, shorter audios also need to to return 4 mels, which will just be a copy
          of the original mel obtained from the padded audio.
              - `rand_trunc` will select a random crop of the mel spectrogram.
      padding (`str`, *optional*, defaults to `"repeatpad"`):
             Padding pattern for shorter audio inputs. Three patterns were originally implemented:
              - `repeatpad`: the audio is repeated, and then padded to fit the `max_length`.
              - `repeat`: the audio is repeated and then cut to fit the `max_length`
              - `pad`: the audio is padded.
code: def _random_mel_fusion(self, mel, total_frames, chunk_frames):
      ranges = np.array_split(
          list(range(0, total_frames - chunk_frames + 1)), 3
      )
      if len(ranges[1]) == 0:
          # if the audio is too short, we just use the first chunk
          ranges[1] = [0]
      if len(ranges[2]) == 0:
          # if the audio is too short, we just use the first chunk
          ranges[2] = [0]
      # randomly choose index for each part
      idx_front = np.random.choice(ranges[0])
      idx_middle = np.random.choice(ranges[1])
      idx_back = np.random.choice(ranges[2])
  
      mel_chunk_front = mel[idx_front : idx_front + chunk_frames, :]
      mel_chunk_middle = mel[idx_middle : idx_middle + chunk_frames, :]
      mel_chunk_back = mel[idx_back : idx_back + chunk_frames, :]
  
      mel = torch.tensor(mel[None, None, :])
      mel_shrink = torch.nn.functional.interpolate(
          mel,
          size=[chunk_frames, 64],
          mode="bilinear",
          align_corners=False,
      )
      mel_shrink = mel_shrink[0][0].numpy()
      mel_fusion = np.stack(
          [
              mel_shrink,
              mel_chunk_front,
              mel_chunk_middle,
              mel_chunk_back,
          ],
          axis=0,
      )
      return mel_fusion
  
file_path: transformers/src/transformers/models/clap/feature_extraction_clap.py
imports: import copy
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import torch
  from ...audio_utils import mel_filter_bank, spectrogram, window_function
  from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
  from ...feature_extraction_utils import BatchFeature
  from ...utils import TensorType, logging
label_desc: Use image node_id 4 for calling the ClapFeatureExtractor obj's underlying member method code with example usage: obj._random_mel_fusion(mel, total_frames, chunk_frames) and returns: mel_fusion