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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def netlsd(inp, timescales=np.logspace(-2, 2, 250), kernel='heat', eigenvalues='auto', normalization='empty', normalized_laplacian=True):
 """ Computes NetLSD signature from some given input, timescales, and normalization. Accepts matrices, common Python graph libraries' graphs, or vectors of eigenvalues. For precise definition, please refer to "NetLSD: Hearing the Shape of a Graph" by A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, E. Müller. Published at KDD'18. Parameters inp: obj 2D numpy/scipy matrix, common Python graph libraries' graph, or vector of eigenvalues timescales : numpy.ndarray Vector of discrete timesteps for the kernel computation kernel : str Either 'heat' or 'wave'. Type of a kernel to use for computation. eigenvalues : str Either string or int or tuple Number of eigenvalues to compute / use for approximation. If string, we expect either 'full' or 'auto', otherwise error will be raised. 'auto' lets the program decide based on the faithful usage. 'full' computes all eigenvalues. If int, compute n_eivals eigenvalues from each side and approximate using linear growth approximation. If tuple, we expect two ints, first for lower part of approximation, and second for the upper part. normalization : str or numpy.ndarray Either 'empty', 'complete' or None. If None or any ther value, return unnormalized heat kernel trace. For the details how 'empty' and 'complete' are computed, please refer to the paper. If np.ndarray, they are treated as exact normalization constants normalized_laplacian: bool Defines whether the eigenvalues came from the normalized Laplacian. It only affects 'complete' normalization. Returns ------- numpy.ndarray NetLSD signature """

    if kernel not in {'heat', 'wave'}:
        raise AttributeError('Unirecognized kernel type: expected one of [\'heat\', \'wave\'], got {0}'.format(kernel))
    if not isinstance(normalized_laplacian, bool):
        raise AttributeError('Unknown Laplacian type: expected bool, got {0}'.format(normalized_laplacian))
    if not isinstance(eigenvalues, (int, tuple, str)):
        raise AttributeError('Unirecognized requested eigenvalue number: expected type of [\'str\', \'tuple\', or \'int\'], got {0}'.format(type(eigenvalues)))
    if not isinstance(timescales, np.ndarray):
        raise AttributeError('Unirecognized timescales data type: expected np.ndarray, got {0}'.format(type(timescales)))
    if timescales.ndim != 1:
        raise AttributeError('Unirecognized timescales dimensionality: expected a vector, got {0}-d array'.format(timescales.ndim))
    if normalization not in {'complete', 'empty', 'none', True, False, None}:
        if not isinstance(normalization, np.ndarray):
            raise AttributeError('Unirecognized normalization type: expected one of [\'complete\', \'empty\', None or np.ndarray], got {0}'.format(normalization))
        if normalization.ndim != 1:
            raise AttributeError('Unirecognized normalization dimensionality: expected a vector, got {0}-d array'.format(normalization.ndim))
        if timescales.shape[0] != normalization.shape[0]:
            raise AttributeError('Unirecognized normalization dimensionality: expected {0}-length vector, got length {1}'.format(timescales.shape[0], normalization.shape[0]))

    eivals = check_1d(inp)
    if eivals is None:
        mat = check_2d(inp)
        if mat is None:
            mat = graph_to_laplacian(inp, normalized_laplacian)
            if mat is None:
                raise ValueError('Unirecognized input type: expected one of [\'np.ndarray\', \'scipy.sparse\', \'networkx.Graph\',\' graph_tool.Graph,\' or \'igraph.Graph\'], got {0}'.format(type(inp)))
        else:
            mat = mat_to_laplacian(inp, normalized_laplacian)
        eivals = eigenvalues_auto(mat, eigenvalues)
    if kernel == 'heat':
        return _hkt(eivals, timescales, normalization, normalized_laplacian)
    else:
        return _wkt(eivals, timescales, normalization, normalized_laplacian)