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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 pyramid_analysis(Gs, f, **kwargs):
 r"""Compute the graph pyramid transform coefficients. Parameters Gs : list of graphs A multiresolution sequence of graph structures. f : ndarray Graph signal to analyze. h_filters : list A list of filter that will be used for the analysis and sythesis operator. If only one filter is given, it will be used for all levels. Default is h(x) = 1 / (2x+1) Returns ------- ca : ndarray Coarse approximation at each level pe : ndarray Prediction error at each level h_filters : list Graph spectral filters applied References See :cite:`shuman2013framework` and :cite:`pesenson2009variational`. """

    if np.shape(f)[0] != Gs[0].N:
        raise ValueError("PYRAMID ANALYSIS: The signal to analyze should have the same dimension as the first graph.")

    levels = len(Gs) - 1

    # check if the type of filters is right.
    h_filters = kwargs.pop('h_filters', lambda x: 1. / (2*x+1))

    if not isinstance(h_filters, list):
        if hasattr(h_filters, '__call__'):
            logger.warning('Converting filters into a list.')
            h_filters = [h_filters]
        else:
            logger.error('Filters must be a list of functions.')

    if len(h_filters) == 1:
        h_filters = h_filters * levels

    elif len(h_filters) != levels:
        message = 'The number of filters must be one or equal to {}.'.format(levels)
        raise ValueError(message)

    ca = [f]
    pe = []

    for i in range(levels):
        # Low pass the signal
        s_low = _analysis(filters.Filter(Gs[i], h_filters[i]), ca[i], **kwargs)
        # Keep only the coefficient on the selected nodes
        ca.append(s_low[Gs[i+1].mr['idx']])
        # Compute prediction
        s_pred = interpolate(Gs[i], ca[i+1], Gs[i+1].mr['idx'], **kwargs)
        # Compute errors
        pe.append(ca[i] - s_pred)

    return ca, pe