code: def stft(
      x,
      sequence_length,
      sequence_stride,
      fft_length,
      window="hann",
      center=True,
  ):
      if standardize_dtype(x.dtype) not in {"float32", "float64"}:
          raise TypeError(
              "Invalid input type. Expected `float32` or `float64`. "
              f"Received: input type={x.dtype}"
          )
      if fft_length < sequence_length:
          raise ValueError(
              "`fft_length` must equal or larger than `sequence_length`. "
              f"Received: sequence_length={sequence_length}, "
              f"fft_length={fft_length}"
          )
      if isinstance(window, str):
          if window not in {"hann", "hamming"}:
              raise ValueError(
                  "If a string is passed to `window`, it must be one of "
                  f'`"hann"`, `"hamming"`. Received: window={window}'
              )
      x = convert_to_tensor(x)
  
      if window is not None:
          if isinstance(window, str):
              if window == "hann":
                  win = torch.hann_window(
                      sequence_length,
                      periodic=True,
                      dtype=x.dtype,
                      device=get_device(),
                  )
              else:
                  win = torch.hamming_window(
                      sequence_length,
                      periodic=True,
                      dtype=x.dtype,
                      device=get_device(),
                  )
          else:
              win = convert_to_tensor(window, dtype=x.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}"
              )
      else:
          win = torch.ones(
              (sequence_length,), dtype=x.dtype, device=get_device()
          )
  
      need_unpack = False
      *batch_shape, samples = x.shape
      if len(x.shape) > 2:
          need_unpack = True
          flat_batchsize = math.prod(batch_shape)
          x = torch.reshape(x, (flat_batchsize, samples))
  
      x = torch.stft(
          x,
          n_fft=fft_length,
          hop_length=sequence_stride,
          win_length=sequence_length,
          window=win,
          center=center,
          return_complex=True,
      )
      if need_unpack:
          fft_unique_bins, num_sequences = x.shape[-2:]
          x = torch.reshape(
              x, (*batch_shape, fft_unique_bins, num_sequences)
          )
  
      x = torch.swapaxes(x, -2, -1)
      return x.real, x.imag
  
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 14 for calling a global function with example usage: stft(x, sequence_length, sequence_stride, fft_length, window, center) and returns: x,  x