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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 _init_args_from_config(cls, cp):
 """Helper function for loading parameters. This retrieves the prior, variable parameters, static parameterss, constraints, sampling transforms, and waveform transforms (if provided). Parameters cp : ConfigParser Config parser to read. Returns ------- dict : Dictionary of the arguments. Has keys ``variable_params``, ``static_params``, ``prior``, and ``sampling_transforms``. If waveform transforms are in the config file, will also have ``waveform_transforms``. """

        section = "model"
        prior_section = "prior"
        vparams_section = 'variable_params'
        sparams_section = 'static_params'
        constraint_section = 'constraint'
        # check that the name exists and matches
        name = cp.get(section, 'name')
        if name != cls.name:
            raise ValueError("section's {} name does not match mine {}".format(
                             name, cls.name))
        # get model parameters
        variable_params, static_params = distributions.read_params_from_config(
            cp, prior_section=prior_section, vargs_section=vparams_section,
            sargs_section=sparams_section)
        # get prior
        prior = cls.prior_from_config(cp, variable_params, prior_section,
                                      constraint_section)
        args = {'variable_params': variable_params,
                'static_params': static_params,
                'prior': prior}
        # try to load sampling transforms
        try:
            sampling_transforms = SamplingTransforms.from_config(
                cp, variable_params)
        except ValueError:
            sampling_transforms = None
        args['sampling_transforms'] = sampling_transforms
        # get any waveform transforms
        if any(cp.get_subsections('waveform_transforms')):
            logging.info("Loading waveform transforms")
            args['waveform_transforms'] = \
                transforms.read_transforms_from_config(
                    cp, 'waveform_transforms')
        return args