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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 connectivity(measure_names, b, c=None, nfft=512):
 """Calculate connectivity measures. Parameters measure_names : str or list of str Name(s) of the connectivity measure(s) to calculate. See :class:`Connectivity` for supported measures. b : array, shape (n_channels, n_channels * model_order) VAR model coefficients. See :ref:`var-model-coefficients` for details about the arrangement of coefficients. c : array, shape (n_channels, n_channels), optional Covariance matrix of the driving noise process. Identity matrix is used if set to None (default). nfft : int, optional Number of frequency bins to calculate. Note that these points cover the range between 0 and half the sampling rate. Returns ------- result : array, shape (n_channels, n_channels, `nfft`) An array of shape (m, m, nfft) is returned if measures is a string. If measures is a list of strings, a dictionary is returned, where each key is the name of the measure, and the corresponding values are arrays of shape (m, m, nfft). Notes ----- When using this function, it is more efficient to get several measures at once than calling the function multiple times. Examples -------- """

    con = Connectivity(b, c, nfft)
    try:
        return getattr(con, measure_names)()
    except TypeError:
        return dict((m, getattr(con, m)()) for m in measure_names)