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 = tf.math.real(complex_input).dtype
  
      expected_output_len = fft_length + sequence_stride * (
          tf.shape(complex_input)[-2] - 1
      )
      l_pad = (fft_length - sequence_length) // 2
      r_pad = fft_length - sequence_length - l_pad
  
      if window is not None:
          if isinstance(window, str):
              if window == "hann":
                  win_array = tf.signal.hann_window(
                      sequence_length, periodic=True, dtype=dtype
                  )
              else:
                  win_array = tf.signal.hamming_window(
                      sequence_length, periodic=True, dtype=dtype
                  )
          else:
              win_array = convert_to_tensor(window, dtype=dtype)
          if (
              len(win_array.shape) != 1
              or win_array.shape[-1] != sequence_length
          ):
              raise ValueError(
                  "The shape of `window` must be equal to [sequence_length]."
                  f"Received: window shape={win_array.shape}"
              )
          win_array = tf.pad(win_array, [[l_pad, r_pad]])
          win = tf.signal.inverse_stft_window_fn(
              sequence_stride, lambda frame_step, dtype: win_array
          )
      else:
          win = None
  
      x = tf.signal.inverse_stft(
          complex_input,
          frame_length=(sequence_length + l_pad + r_pad),
          frame_step=sequence_stride,
          fft_length=fft_length,
          window_fn=win,
      )
  
      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]
  
file_path: keras/keras/backend/tensorflow/math.py
imports: import tensorflow
  from keras.backend import standardize_dtype
  from keras.backend.tensorflow.core import convert_to_tensor
label_desc: Use image node_id 14 for calling a global function with example usage: istft(x, sequence_length, sequence_stride, fft_length, length, window, center) and returns: x