code: def overlap_and_add(signal, frame_step):
      """Reconstructs a signal from a framed representation.
  
          Adds potentially overlapping frames of a signal with shape
          `[..., frames, frame_length]`, offsetting subsequent frames by `frame_step`.
          The resulting tensor has shape `[..., output_size]` where
              output_size = (frames - 1) * frame_step + frame_length
  
          Args:
              signal: A [..., frames, frame_length] Tensor. All dimensions may be unknown,
                  and rank must be at least 2.
              frame_step: An integer denoting overlap offsets.
                  Must be less than or equal to frame_length.
  
          Returns:
              A Tensor with shape [..., output_size] containing the
                  overlap-added frames of signal's inner-most two dimensions.
              output_size = (frames - 1) * frame_step + frame_length
  
          Based on
  
      https://github.com/tensorflow/tensorflow/blob/r1.12/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
      """
      outer_dimensions = signal.size()[:-2]
      frames, frame_length = signal.size()[-2:]
  
      # gcd=Greatest Common Divisor
      subframe_length = math.gcd(frame_length, frame_step)
      subframe_step = frame_step // subframe_length
      subframes_per_frame = frame_length // subframe_length
      output_size = frame_step * (frames - 1) + frame_length
      output_subframes = output_size // subframe_length
  
      subframe_signal = signal.view(
          *outer_dimensions, -1, subframe_length
      )
  
      frame = torch.arange(0, output_subframes).unfold(
          0, subframes_per_frame, subframe_step
      )
      frame = frame.clone().detach().long().to(signal.device)
      # frame = signal.new_tensor(frame).clone().long()  # signal may in GPU or CPU
      frame = frame.contiguous().view(-1)
  
      result = signal.new_zeros(
          *outer_dimensions, output_subframes, subframe_length
      )
      result.index_add_(-2, frame, subframe_signal)
      result = result.view(*outer_dimensions, -1)
      return result
  
imports: import math
  from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers.dpmulcat import DPMulCat
  from espnet2.enh.layers.dprnn import merge_feature, split_feature
  from espnet2.enh.separator.abs_separator import AbsSeparator
label_desc: Use image node_id 1 for calling a global function with example usage: overlap_and_add(signal, frame_step) and returns: result