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",")"]
docstr: Arguments
  ---------
  waveforms : tensor
      Shape should be `[batch, time]` or `[batch, time, channels]`.
  
  Returns
  -------
  Tensor of shape `[batch, time]` or `[batch, time, channels]`
docstr_tok: ["Arguments","--","--","--","--","-","waveforms",":","tensor","Shape","should","be","`","[","batch",",","time","]","`","or","`","[","batch",",","time",",","channels","]","`",".","Returns","--","--","--","-","Tensor","of","shape","`","[","batch",",","time","]","`","or","`","[","batch",",","time",",","channels","]","`"]
code: def forward(self, waveforms):
      """
      Arguments
      ---------
      waveforms : tensor
          Shape should be `[batch, time]` or `[batch, time, channels]`.
  
      Returns
      -------
      Tensor of shape `[batch, time]` or `[batch, time, channels]`
      """
  
      # Pick a channel to drop
      x = torch.rand(waveforms.shape[-1], device=waveforms.device)
      channel_mask = x.ge(self.drop_rate)
      waveforms = waveforms * channel_mask.unsqueeze(0).unsqueeze(1)
      return waveforms
  
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 2 for calling the ChannelDrop obj's underlying member method code with example usage: obj.forward(waveforms) and returns: waveforms