following expression:
  X[\omega, m] = \sum_{k = 0}^{\text{win\_length-1}}%
  \text{window}[k]\ \text{input}[m \times \text{hop\_length} + k]\
  %                     \exp\left(- j \frac{2 \pi \cdot \omega
  k}{\text{win\_length}}\right),

where m is the index of the sliding window, and \omega is the
   frequency 0 \leq \omega < \text{n_fft} for "onesided=False", or 0
   \leq \omega < \lfloor \text{n_fft} / 2 \rfloor + 1 for
   "onesided=True".


"input" must be either a 1-D time sequence or a 2-D batch of time
     sequences.


If "hop_length" is "None" (default), it is treated as equal to
     "floor(n_fft / 4)".


If "win_length" is "None" (default), it is treated as equal to
     "n_fft".


"window" can be a 1-D tensor of size "win_length", e.g., from
     "torch.hann_window()". If "window" is "None" (default), it is
     treated as if having 1 everywhere in the window. If
     \text{win_length} < \text{n_fft}, "window" will be padded on

