code: def funcSmthTmp(aryFuncChnk, varSdSmthTmp):
      
      # For the filtering to perform well at the ends of the time series, we
      # set the method to 'nearest' and place a volume with mean intensity
      # (over time) at the beginning and at the end.
      aryFuncChnkMean = np.mean(aryFuncChnk,
                                axis=1,
                                keepdims=True)
  
      aryFuncChnk = np.concatenate((aryFuncChnkMean,
                                    aryFuncChnk,
                                    aryFuncChnkMean), axis=1)
  
      # In the input data, time goes from left to right. Therefore, we apply
      # the filter along axis=1.
      aryFuncChnk = gaussian_filter1d(aryFuncChnk,
                                      varSdSmthTmp,
                                      axis=1,
                                      order=0,
                                      mode='nearest',
                                      truncate=4.0)
  
      # Remove mean-intensity volumes at the beginning and at the end:
      aryFuncChnk = aryFuncChnk[:, 1:-1]
  
      # Output list:
      return aryFuncChnk
docs: Apply temporal smoothing to fMRI data & pRF time course models.
  
      Parameters
      ----------
      aryFuncChnk : np.array
          TODO
      varSdSmthTmp : float (?)
          extend of smoothing
  
      Returns
      -------
      aryFuncChnk : np.array
          TODO