code: def split_bands(
      signal: torch.Tensor,
      sample_rate: float,
      n_bands: Optional[int] = None,
      cutoffs: Optional[Sequence[float]] = None,
      pad: bool = True,
      zeros: float = 8,
      fft: Optional[bool] = None,
  ):
      """
      Functional version of `SplitBands`, refer to this class for more information.
  
      >>> x = torch.randn(6, 4, 1024)
      >>> list(split_bands(x, sample_rate=64, cutoffs=[12, 24]).shape)
      [3, 6, 4, 1024]
      """
      return SplitBands(
          sample_rate, n_bands, cutoffs, pad, zeros, fft
      ).to(signal)(signal)
  
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 1 for calling a global function with example usage: split_bands(signal, sample_rate, n_bands, cutoffs, pad, zeros, fft) and returns: SplitBands