code: def evaluate(self, x):
          r
          x = np.asanyarray(x)
          # Avoid to copy data as with np.array([g(x) for g in self._kernels]).
          y = np.empty([self.Nf] + list(x.shape))
          for i, kernel in enumerate(self._kernels):
              y[i] = kernel(x)
          return y
docs: r"""Evaluate the kernels at given frequencies.
  
          Parameters
          ----------
          x : array_like
              Graph frequencies at which to evaluate the filter.
  
          Returns
          -------
          y : ndarray
              Frequency response of the filters. Shape ``(g.Nf, len(x))``.
  
          Examples
          --------
          Frequency response of a low-pass filter:
  
          >>> import matplotlib.pyplot as plt
          >>> G = graphs.Logo()
          >>> G.compute_fourier_basis()
          >>> f = filters.Expwin(G)
          >>> G.compute_fourier_basis()
          >>> y = f.evaluate(G.e)
          >>> plt.plot(G.e, y[0])  # doctest: +ELLIPSIS
          [<matplotlib.lines.Line2D object at ...>]