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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 options(self):
 """ Dictionary of options which affect the curve fitting algorithm. Must contain the key `fit_function` which must be set to the function that will perform the fit. All other options are passed as keyword arguments to the `fit_function`. The default options use `scipy.optimize.curve_fit`. If `fit_function` has the special value `lmfit`, then [lmfit][1] is used for the fit and all other options are passed as keyword arguments to [`lmfit.minimize`][2]. When using [lmfit][1], additional control of the fit is obtained by overriding `scipy_data_fitting.Fit.lmfit_fcn2min`. Any other function may be used for `fit_function` that satisfies the following criteria: * Must accept the following non-keyword arguments in this order (even if unused in the fitting function):
 1. Function to fit, see `scipy_data_fitting.Fit.function`. 2. Independent values: see `scipy_data_fitting.Data.array`. 3. Dependent values: see `scipy_data_fitting.Data.array`. 4. List of the initial fitting parameter guesses in same order as given by `scipy_data_fitting.Fit.fitting_parameters`. The initial guesses will be scaled by their prefix before being passed. * Can accept any keyword arguments set in `scipy_data_fitting.Fit.options`. For example, this is how one could pass error values to the fitting function. * Must return an object whose first element is a list or array of the values of the fitted parameters (and only those values) in same order as given by `scipy_data_fitting.Fit.fitting_parameters`. Default: #!python { 'fit_function': scipy.optimize.curve_fit, 'maxfev': 1000, } [1]: http://lmfit.github.io/lmfit-py/ [2]: http://lmfit.github.io/lmfit-py/fitting.html#the-minimize-function """

        if not hasattr(self, '_options'):
            self._options = {
                'fit_function': scipy.optimize.curve_fit,
                'maxfev': 1000,
            }
        return self._options