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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 snr(*args, **kwargs):
 """Compute the SNR of binaries. snr is a function that takes binary parameters and sensitivity curves as inputs, and returns snr for chosen phases. Warning: All binary parameters must be either scalar, len-1 arrays, or arrays of the same length. All of these can be used at once. However, you cannot input multiple arrays of different lengths. Arguments: *args: Arguments for :meth:`gwsnrcalc.utils.pyphenomd.PhenomDWaveforms.__call__` **kwargs: Keyword arguments related to parallel generation (see :class:`gwsnrcalc.utils.parallel`), waveforms (see :class:`gwsnrcalc.utils.pyphenomd`), or sensitivity information (see :class:`gwsnrcalc.utils.sensitivity`). Returns: (dict or list of dict):
 Signal-to-Noise Ratio dictionary for requested phases. """

    squeeze = False
    max_length = 0
    for arg in args:
        try:
            length = len(arg)
            if length > max_length:
                max_length = length

        except TypeError:
            pass

    if max_length == 0:
        squeeze = True

    kwargs['length'] = max_length

    snr_main = SNR(**kwargs)
    if squeeze:
        snr_out = snr_main(*args)
        return {key: np.squeeze(snr_out[key]) for key in snr_out}
    return snr_main(*args)