code: def signal_framing(
      signal: torch.Tensor,
      frame_length: int,
      frame_step: int,
      bdelay: int,
      do_padding: bool = False,
      pad_value: int = 0,
      indices: List = None,
  ) -> torch.Tensor:
      """Expand `signal` into several frames, with each frame of length `frame_length`.
  
      Args:
          signal : (..., T)
          frame_length:   length of each segment
          frame_step:     step for selecting frames
          bdelay:         delay for WPD
          do_padding:     whether or not to pad the input signal at the beginning
                            of the time dimension
          pad_value:      value to fill in the padding
  
      Returns:
          torch.Tensor:
              if do_padding: (..., T, frame_length)
              else:          (..., T - bdelay - frame_length + 2, frame_length)
      """
      frame_length2 = frame_length - 1
      # pad to the right at the last dimension of `signal` (time dimension)
      if do_padding:
          # (..., T) --> (..., T + bdelay + frame_length - 2)
          signal = torch.nn.functional.pad(
              signal,
              (bdelay + frame_length2 - 1, 0),
              "constant",
              pad_value,
          )
          do_padding = False
  
      if indices is None:
          # [[ 0, 1, ..., frame_length2 - 1,              frame_length2 - 1 + bdelay ],
          #  [ 1, 2, ..., frame_length2,                  frame_length2 + bdelay     ],
          #  [ 2, 3, ..., frame_length2 + 1,              frame_length2 + 1 + bdelay ],
          #  ...
          #  [ T-bdelay-frame_length2, ..., T-1-bdelay,   T-1 ]]
          indices = [
              [
                  *range(i, i + frame_length2),
                  i + frame_length2 + bdelay - 1,
              ]
              for i in range(
                  0,
                  signal.shape[-1] - frame_length2 - bdelay + 1,
                  frame_step,
              )
          ]
  
      if torch.is_complex(signal):
          real = signal_framing(
              signal.real,
              frame_length,
              frame_step,
              bdelay,
              do_padding,
              pad_value,
              indices,
          )
          imag = signal_framing(
              signal.imag,
              frame_length,
              frame_step,
              bdelay,
              do_padding,
              pad_value,
              indices,
          )
          return torch.complex(real, imag)
      else:
          # (..., T - bdelay - frame_length + 2, frame_length)
          signal = signal[..., indices]
          return signal
  
imports: from typing import List, Union
  import torch
  import torch_complex.functional
  import torchaudio
label_desc: Use image node_id 15 for calling a global function with example usage: signal_framing(signal, frame_length, frame_step, bdelay, do_padding, pad_value, indices) and returns: torch, signal