class_docstr: Adjust the volume of different frequency bands. This transform is a 7-band
  parametric equalizer - a combination of one low shelf filter, five peaking filters
  and one high shelf filter, all with randomized gains, Q values and center frequencies.
  
  Because this transform changes the timbre, but keeps the overall "class" of the
  sound the same (depending on application), it can be used for data augmentation to
  make ML models more robust to various frequency spectrums. Many things can affect
  the spectrum, for example:
  
  * the nature and quality of the sound source
  * room acoustics
  * any objects between the microphone and the sound source
  * microphone type/model
  * the distance between the sound source and the microphone
  
  The seven bands have center frequencies picked in the following ranges (min-max):
  
  * 42-95 Hz
  * 91-204 Hz
  * 196-441 Hz
  * 421-948 Hz
  * 909-2045 Hz
  * 1957-4404 Hz
  * 4216-9486 Hz
code: def apply(self, samples: NDArray[np.float32], sample_rate: int):
      samples = self.low_shelf_filter(samples, sample_rate)
      for i in range(len(self.peaking_filters)):
          samples = self.peaking_filters[i](samples, sample_rate)
      samples = self.high_shelf_filter(samples, sample_rate)
      return samples
  
file_path: audiomentations/audiomentations/augmentations/seven_band_parametric_eq.py
imports: import numpy
  from numpy.typing import NDArray
  from audiomentations import LowShelfFilter, PeakingFilter, HighShelfFilter
  from audiomentations.core.transforms_interface import BaseWaveformTransform
label_desc: Use image node_id 3 for calling the SevenBandParametricEQ obj's underlying member method code with example usage: obj.apply(samples, sample_rate) and returns: samples