code: def funcSmthSpt(aryFuncChnk, varSdSmthSpt):
      
      varNdim = aryFuncChnk.ndim
  
      # Number of time points in this chunk:
      varNumVol = aryFuncChnk.shape[-1]
  
      # Loop through volumes:
      if varNdim == 4:
          for idxVol in range(0, varNumVol):
  
              aryFuncChnk[:, :, :, idxVol] = gaussian_filter(
                  aryFuncChnk[:, :, :, idxVol],
                  varSdSmthSpt,
                  order=0,
                  mode='nearest',
                  truncate=4.0)
      elif varNdim == 5:
          varNumMtnDrctns = aryFuncChnk.shape[3]
          for idxVol in range(0, varNumVol):
              for idxMtn in range(0, varNumMtnDrctns):
                  aryFuncChnk[:, :, :, idxMtn, idxVol] = gaussian_filter(
                      aryFuncChnk[:, :, :, idxMtn, idxVol],
                      varSdSmthSpt,
                      order=0,
                      mode='nearest',
                      truncate=4.0)
  
      # Output list:
      return aryFuncChnk
docs: Apply spatial smoothing to the input data.
  
      Parameters
      ----------
      aryFuncChnk : np.array
          TODO
      varSdSmthSpt : float (?)
          Extent of smoothing.
      Returns
      -------
      aryFuncChnk : np.array
          Smoothed data.