class_docstr: This function drops random channels in the multi-channel nput waveform.
  
  Arguments
  ---------
  drop_rate : float
      The channel droput factor
  
  Example
  -------
  >>> signal = torch.rand(4, 256, 8)
  >>> ch_drop = ChannelDrop(drop_rate=0.5)
  >>> output_signal = ch_drop(signal)
class_docstr_tok: ["This","function","drops","random","channels","in","the","multi-channel","nput","waveform",".","Arguments","--","--","--","--","-","drop_rate",":","float","The","channel","droput","factor","Example","--","--","--","-",">",">",">","signal","=","torch.rand","(","4",",","256",",","8",")",">",">",">","ch_drop","=","ChannelDrop","(","drop_rate=0.5",")",">",">",">","output_signal","=","ch_drop","(","signal",")"]
code: def __init__(self, drop_rate=0.1):
      super().__init__()
      self.drop_rate = drop_rate
  
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 ChannelDrop object from inherited base classes: torch.nn with example: obj = ChannelDrop(drop_rate)