<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def compute_frame(self, **kwargs):
 r"""Compute the associated frame. A filter bank defines a frame, which is a generalization of a basis to sets of vectors that may be linearly dependent. See `Wikipedia <https://en.wikipedia.org/wiki/Frame_(linear_algebra)>`_. The frame of a filter bank is the union of the frames of its constituent filters. The vectors forming the frame are the rows of the *analysis operator* .. math:: g(L) = \begin{pmatrix} g_1(L) \\ \vdots \\ g_F(L) \end{pmatrix} \in \mathbb{R}^{NF \times N}, \quad g_i(L) = U g_i(\Lambda) U^\top, where :math:`g_i` are the filter kernels, :math:`N` is the number of nodes, :math:`F` is the number of filters, :math:`L` is the graph Laplacian, :math:`\Lambda` is a diagonal matrix of the Laplacian's eigenvalues, and :math:`U` is the Fourier basis, i.e., its columns are the eigenvectors of the Laplacian. The matrix :math:`g(L)` represents the *analysis operator* of the frame. Its adjoint :math:`g(L)^\top` represents the *synthesis operator*. A signal :math:`x` is thus analyzed with the frame by :math:`y = g(L) x`, and synthesized from its frame coefficients by :math:`z = g(L)^\top y`. Computing this matrix is however a rather inefficient way of doing those operations. If :math:`F > 1`, the frame is said to be over-complete and the representation :math:`g(L) x` of the signal :math:`x` is said to be redundant. If the frame is tight, the *frame operator* :math:`g(L)^\top g(L)` is diagonal, with entries equal to the frame bound :math:`A = B`. Parameters kwargs: dict Parameters to be passed to the :meth:`analyze` method. Returns ------- frame : ndarray Array of size (#nodes x #filters) x #nodes. See Also -------- estimate_frame_bounds: estimate the frame bounds filter: more efficient way to filter signals Examples -------- Filtering as a multiplication with the matrix representation of the frame analysis operator: (600, 100) True The frame operator of a tight frame is the identity matrix times the frame bound: A=1.000, B=1.000 (600, 100) True """

        if self.G.N > 2000:
            _logger.warning('Creating a big matrix. '
                            'You should prefer the filter method.')

        # Filter one delta per vertex.
        s = np.identity(self.G.N)
        return self.filter(s, **kwargs).T.reshape(-1, self.G.N)