code: def _check_input_st(x_in, n_fft):
      """Aux function."""
      # flatten to 2 D and memorize original shape
      n_times = x_in.shape[-1]
  
      def _is_power_of_two(n):
          return not (n > 0 and (n & (n - 1)))
  
      if n_fft is None or (
          not _is_power_of_two(n_fft) and n_times > n_fft
      ):
          # Compute next power of 2
          n_fft = 2 ** int(np.ceil(np.log2(n_times)))
      elif n_fft < n_times:
          raise ValueError(
              "n_fft cannot be smaller than signal size. "
              "Got %s < %s." % (n_fft, n_times)
          )
      if n_times < n_fft:
          logger.info(
              f'The input signal is shorter ({x_in.shape[-1]}) than "n_fft" ({n_fft}). '
              "Applying zero padding."
          )
          zero_pad = n_fft - n_times
          pad_array = np.zeros(
              x_in.shape[:-1] + (zero_pad,), x_in.dtype
          )
          x_in = np.concatenate((x_in, pad_array), axis=-1)
      else:
          zero_pad = 0
      return x_in, n_fft, zero_pad
  
file_path: mne-python/mne/time_frequency/_stockwell.py
imports: from copy import deepcopy
  import numpy
  from scipy.fft import fft, fftfreq, ifft
  from .._fiff.pick import _pick_data_channels, pick_info
  from ..parallel import parallel_func
  from ..utils import _validate_type, fill_doc, logger, verbose
  from .tfr import AverageTFR, _get_data
label_desc: Use image node_id 1 for calling a global function with example usage: _check_input_st(x_in, n_fft) and returns: x_in,  n_fft,  zero_pad, unknown