code: def _call(self, coeffs):
          
          if self.impl == 'pywt':
              coeffs = pywt.unravel_coeffs(coeffs,
                                           coeff_slices=self._coeff_slices,
                                           coeff_shapes=self._coeff_shapes,
                                           output_format='wavedecn')
              recon = pywt.waverecn(
                  coeffs, wavelet=self.pywt_wavelet, mode=self.pywt_pad_mode,
                  axes=self.axes)
              recon_shape = self.range.shape
              if recon.shape != recon_shape:
                  # If the original shape was odd along any transformed axes it
                  # will have been rounded up to the next even size after the
                  # reconstruction. The extra sample should be discarded.
                  # The underlying reason is decimation by two in reconstruction
                  # must keep ceil(N/2) samples in each band for perfect
                  # reconstruction. Reconstruction then upsamples by two.
                  # When N is odd, (2 * np.ceil(N/2)) != N.
                  recon_slc = []
                  for i, (n_recon, n_intended) in enumerate(zip(recon.shape,
                                                                recon_shape)):
                      if n_recon == n_intended + 1:
                          # Upsampling added one entry too much in this axis,
                          # drop last one
                          recon_slc.append(slice(-1))
                      elif n_recon == n_intended:
                          recon_slc.append(slice(None))
                      else:
                          raise ValueError(
                              'in axis {}: expected size {} or {} in '
                              '`recon_shape`, got {}'
                              ''.format(i, n_recon - 1, n_recon,
                                        n_intended))
                  recon = recon[tuple(recon_slc)]
              return recon
          else:
              raise RuntimeError("bad `impl` '{}'".format(self.impl))
docs: Return the inverse wavelet transform of ``coeffs``.