class_docstr: This function combines segments (with equal length in time) of the time series contained in the batch.
  Proposed for EEG signals in https://doi.org/10.1016/j.neunet.2021.05.032.
  
  Arguments
  ---------
  min_num_segments : int
      The number of segments to combine.
  max_num_segments : int
      The maximum number of segments to combine. Default is 10.
  
  Example
  -------
  >>> signal = torch.ones((4, 256, 22)) * torch.arange(4).reshape((4, 1, 1,))
  >>> cutcat =  CutCat()
  >>> output_signal = cutcat(signal)
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code: def __init__(self, min_num_segments=2, max_num_segments=10):
      super().__init__()
      self.min_num_segments = min_num_segments
      self.max_num_segments = max_num_segments
      # Check arguments
      if self.max_num_segments < self.min_num_segments:
          raise ValueError(
              "max_num_segments must be  >= min_num_segments"
          )
  
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 CutCat object from inherited base classes: torch.nn with example: obj = CutCat(min_num_segments, max_num_segments)