How to use the petab.C.ESTIMATE function in petab

Returns
        -------
        prior:
            A valid pyabc.Distribution for the parameters to estimate.
        """
        # add default values
        parameter_df = petab.normalize_parameter_df(
            self.petab_problem.parameter_df)

        prior_dct = {}

        # iterate over parameters
        for _, row in parameter_df.reset_index().iterrows():
            # check whether we can ignore
            if not self.fixed_parameters and row[petab.C.ESTIMATE] == 0:
                # ignore fixed parameters
                continue
            if not self.free_parameters and row[petab.C.ESTIMATE] == 1:
                # ignore free parameters
                continue

            # pyabc currently only knows objective priors, no
            #  initialization priors
            prior_type = row[petab.C.OBJECTIVE_PRIOR_TYPE]
            pars_str = row[petab.C.OBJECTIVE_PRIOR_PARAMETERS]
            prior_pars = tuple(float(val) for val in pars_str.split(';'))

            # create random variable from table entry
            if prior_type in [petab.C.PARAMETER_SCALE_UNIFORM,
                              petab.C.UNIFORM]:
                lb, ub = prior_pars

self.minimize_options = minimize_options

        self.model_id = self.row[MODEL_ID]

        self._AIC = None
        self._BIC = None

        if self.valid:
            # TODO warning/error if x_fixed_estimated is not a parameter ID in
            # the PEtab parameter table. A warning is currently produced in
            # `row2problem` above.
            # Could move to a separate method that is only called when a
            # criterion that requires the number of estimated parameters is
            # called (same for self.n_measurements).
            self.estimated = x_fixed_estimated | set(
                self.petab_problem.parameter_df.query(f'{ESTIMATE} == 1').index
            )
            self.n_estimated = len(self.estimated)
            self.n_measurements = len(petab_problem.measurement_df)

            self.pypesto_problem = row2problem(row,
                                               petab_problem,
                                               x_guess=x_guess)

            self.minimize_result = None

            # TODO autorun may be unnecessary now that the `minimize_options`
            # argument is implemented.
            if autorun:
                if minimize_options:
                    self.set_result(minimize(self.pypesto_problem,
                                             **minimize_options))

for key in [YAML_FILENAME_COLUMN, MODEL_NAME_COLUMN]:
        if key in row.keys():
            row.pop(key)

    for par_id, par_val in row.items():
        if par_id not in petab_problem.x_ids:
            print(Fore.YELLOW + f'Warning: parameter {par_id} is not defined '
                                f'in PETab model. It will be ignored.')
            continue
        if not np.isnan(par_val):
            petab_problem.parameter_df[ESTIMATE].loc[par_id] = 0
            petab_problem.parameter_df[NOMINAL_VALUE].loc[par_id] = par_val
            # petab_problem.parameter_df.lowerBound.loc[par_id] = float("NaN")
            # petab_problem.parameter_df.upperBound.loc[par_id] = float("NaN")
        else:
            petab_problem.parameter_df[ESTIMATE].loc[par_id] = 1
            # petab_problem.parameter_df.nominalValue.loc[par_id] = float(
            # "NaN")

    # chose standard objective in case none is provided
    importer = PetabImporter(petab_problem)
    if obj is None:
        obj = importer.create_objective()
    pypesto_problem = importer.create_problem(obj)

    return pypesto_problem

#        row.pop(key)
    row_parameters = {k: row[k] for k in row if k not in NOT_PARAMETERS}

    for par_id, par_val in row_parameters.items():
    #for par_id, par_val in row.items():
        if par_id not in petab_problem.x_ids:
            logger.info(Fore.YELLOW + f'Warning: parameter {par_id} is not defined '
                                f'in PETab model. It will be ignored.')
            continue
        if not np.isnan(par_val):
            petab_problem.parameter_df[ESTIMATE].loc[par_id] = 0
            petab_problem.parameter_df[NOMINAL_VALUE].loc[par_id] = par_val
            # petab_problem.parameter_df.lowerBound.loc[par_id] = float("NaN")
            # petab_problem.parameter_df.upperBound.loc[par_id] = float("NaN")
        else:
            petab_problem.parameter_df[ESTIMATE].loc[par_id] = 1
            # petab_problem.parameter_df.nominalValue.loc[par_id] = float(
            # "NaN")

    # Any parameter values in `x_guess` for parameters that are not estimated
    # should be filtered out and replaced with
    # - their corresponding values in `row` if possible, else
    # - their corresponding nominal values in the `petab_problem.parameter_df`.
    # TODO reconsider whether filtering is a good idea (x_guess is no longer
    # the latest MLE then). Similar todo exists in
    # `ModelSelectorMethod.new_model_problem`.
    if x_guess is not None:
        filtered_x_guess = []
        for par_id, par_val in x_guess:
            if petab_problem.parameter_df[ESTIMATE].loc[par_id] == 1:
                filtered_x_guess.append(par_val)
            else:

#        row.pop(key)
    row_parameters = {k: row[k] for k in row if k not in NOT_PARAMETERS}

    for par_id, par_val in row_parameters.items():
    #for par_id, par_val in row.items():
        if par_id not in petab_problem.x_ids:
            print(Fore.YELLOW + f'Warning: parameter {par_id} is not defined '
                                f'in PETab model. It will be ignored.')
            continue
        if not np.isnan(par_val):
            petab_problem.parameter_df[ESTIMATE].loc[par_id] = 0
            petab_problem.parameter_df[NOMINAL_VALUE].loc[par_id] = par_val
            # petab_problem.parameter_df.lowerBound.loc[par_id] = float("NaN")
            # petab_problem.parameter_df.upperBound.loc[par_id] = float("NaN")
        else:
            petab_problem.parameter_df[ESTIMATE].loc[par_id] = 1
            # petab_problem.parameter_df.nominalValue.loc[par_id] = float(
            # "NaN")

    # chose standard objective in case none is provided
    importer = PetabImporter(petab_problem)
    if obj is None:
        obj = importer.create_objective()
    pypesto_problem = importer.create_problem(obj)

    return pypesto_problem