code: def istft(
      x,
      sequence_length,
      sequence_stride,
      fft_length,
      length=None,
      window="hann",
      center=True,
  ):
      complex_input = _get_complex_tensor_from_tuple(x)
      dtype = complex_input.real.dtype
      win = None
      if window is not None:
          if isinstance(window, str):
              if window == "hann":
                  win = torch.hann_window(
                      sequence_length,
                      periodic=True,
                      dtype=dtype,
                      device=get_device(),
                  )
              else:
                  win = torch.hamming_window(
                      sequence_length,
                      periodic=True,
                      dtype=dtype,
                      device=get_device(),
                  )
          else:
              win = convert_to_tensor(window, dtype=dtype)
          if len(win.shape) != 1 or win.shape[-1] != sequence_length:
              raise ValueError(
                  "The shape of `window` must be equal to [sequence_length]."
                  f"Received: window shape={win.shape}"
              )
  
      if (
          sequence_length == fft_length
          and center is True
          and win is not None
      ):
          # can be falled back to torch.istft
          need_unpack = False
          (
              *batch_shape,
              num_sequences,
              fft_unique_bins,
          ) = complex_input.shape
          if len(complex_input.shape) > 3:
              need_unpack = True
              flat_batchsize = math.prod(batch_shape)
              complex_input = torch.reshape(
                  complex_input,
                  (flat_batchsize, num_sequences, fft_unique_bins),
              )
          complex_input = torch.swapaxes(complex_input, -2, -1)
          x = torch.istft(
              complex_input,
              n_fft=fft_length,
              hop_length=sequence_stride,
              win_length=sequence_length,
              window=win,
              center=center,
              length=length,
              return_complex=False,
          )
          if need_unpack:
              samples = x.shape[-1]
              x = torch.reshape(x, (*batch_shape, samples))
          return x
  
      # custom implementation with irfft and _overlap_sequences
      # references:
      # torch: aten/src/ATen/native/SpectralOps.cpp
      # tf: tf.signal.inverse_stft_window_fn
      x = irfft(x, fft_length)
  
      expected_output_len = fft_length + sequence_stride * (
          x.shape[-2] - 1
      )
  
      if win is not None:
          l_pad = (fft_length - sequence_length) // 2
          r_pad = fft_length - sequence_length - l_pad
          win = pad(win, [[l_pad, r_pad]], "constant")
  
          # square and sum
          _sequence_length = sequence_length + l_pad + r_pad
          denom = torch.square(win)
          overlaps = -(-_sequence_length // sequence_stride)
          denom = pad(
              denom,
              [(0, overlaps * sequence_stride - _sequence_length)],
          )
          denom = torch.reshape(denom, [overlaps, sequence_stride])
          denom = torch.sum(denom, 0, keepdims=True)
          denom = torch.tile(denom, [overlaps, 1])
          denom = torch.reshape(denom, [overlaps * sequence_stride])
          win = torch.divide(win, denom[:_sequence_length])
          x = torch.multiply(x, win)
  
      x = _overlap_sequences(x, sequence_stride)
  
      start = 0 if center is False else fft_length // 2
      if length is not None:
          end = start + length
      elif center is True:
          end = -(fft_length // 2)
      else:
          end = expected_output_len
      return x[..., start:end]
  
imports: import math
  import torch
  from keras.backend import standardize_dtype
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import pad
label_desc: Use image node_id 15 for calling a global function with example usage: istft(x, sequence_length, sequence_stride, fft_length, length, window, center) and returns: x, x