class_docstr: Decomposes a signal over the given frequency bands in the waveform domain using
  a cascade of low pass filters as implemented by `julius.lowpass.LowPassFilters`.
  You can either specify explicitely the frequency cutoffs, or just the number of bands,
  in which case the frequency cutoffs will be spread out evenly in mel scale.
  
  Args:
      sample_rate (float): Sample rate of the input signal in Hz.
      n_bands (int or None): number of bands, when not giving them explictely with `cutoffs`.
          In that case, the cutoff frequencies will be evenly spaced in mel-space.
      cutoffs (list[float] or None): list of frequency cutoffs in Hz.
      pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
          the output will have the same length as the input.
      zeros (float): Number of zero crossings to keep. See `LowPassFilters` for more informations.
      fft (bool or None): See `LowPassFilters` for more info.
  
  ..note::
      The sum of all the bands will always be the input signal.
  
  ..warning::
      Unlike `julius.lowpass.LowPassFilters`, the cutoffs frequencies must be provided in Hz along
      with the sample rate.
  
  Shape:
  
      - Input: `[*, T]`
      - Output: `[B, *, T']`, with `T'=T` if `pad` is True.
          If `n_bands` was provided, `B = n_bands` otherwise `B = len(cutoffs) + 1`
  
  >>> bands = SplitBands(sample_rate=128, n_bands=10)
  >>> x = torch.randn(6, 4, 1024)
  >>> list(bands(x).shape)
  [10, 6, 4, 1024]
code: def forward(self, input):
      if self.lowpass is None:
          return input[None]
      lows = self.lowpass(input)
      low = lows[0]
      bands = [low]
      for low_and_band in lows[1:]:
          # Get a bandpass filter by substracting lowpasses
          band = low_and_band - low
          bands.append(band)
          low = low_and_band
      # Last band is whatever is left in the signal
      bands.append(input - low)
      return torch.stack(bands)
  
file_path: julius/julius/bands.py
imports: from typing import Optional, Sequence
  import torch
  from .core import mel_frequencies
  from .lowpass import LowPassFilters
  from .utils import simple_repr
label_desc: Use image node_id 2 for calling the SplitBands obj's underlying member method code with example usage: obj.forward(input) and returns: torch, input