class_docstr: This class drops a random frequency from the signal.
  
  The purpose of this class is to teach models to learn to rely on all parts
  of the signal, not just a few frequency bands.
  
  Arguments
  ---------
  drop_freq_low : float
      The low end of frequencies that can be dropped,
      as a fraction of the sampling rate / 2.
  drop_freq_high : float
      The high end of frequencies that can be
      dropped, as a fraction of the sampling rate / 2.
  drop_freq_count_low : int
      The low end of number of frequencies that could be dropped.
  drop_freq_count_high : int
      The high end of number of frequencies that could be dropped.
  drop_freq_width : float
      The width of the frequency band to drop, as
      a fraction of the sampling_rate / 2.
  
  Example
  -------
  >>> from speechbrain.dataio.dataio import read_audio
  >>> dropper = DropFreq()
  >>> signal = read_audio('tests/samples/single-mic/example1.wav')
  >>> dropped_signal = dropper(signal.unsqueeze(0))
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code: def __init__(
      self,
      drop_freq_low=1e-14,
      drop_freq_high=1,
      drop_freq_count_low=1,
      drop_freq_count_high=3,
      drop_freq_width=0.05,
  ):
      super().__init__()
      self.drop_freq_low = drop_freq_low
      self.drop_freq_high = drop_freq_high
      self.drop_freq_count_low = drop_freq_count_low
      self.drop_freq_count_high = drop_freq_count_high
      self.drop_freq_width = drop_freq_width
  
imports: import math
  import random
  import torch
  import torch.nn.functional
  from speechbrain.dataio.legacy import ExtendedCSVDataset
  from speechbrain.dataio.dataloader import make_dataloader
  from speechbrain.processing.signal_processing import compute_amplitude, dB_to_amplitude, convolve1d, notch_filter, reverberate
label_desc: Use image node_id 1 to create a new DropFreq object from inherited base classes: torch.nn with example: obj = DropFreq(drop_freq_low, drop_freq_high, drop_freq_count_low, drop_freq_count_high, drop_freq_width)