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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 fit( self, durations, event_observed=None, timeline=None, entry=None, label="KM_estimate", left_censorship=False, alpha=None, ci_labels=None, weights=None, ):
 # pylint: disable=too-many-arguments,too-many-locals """ Fit the model to a right-censored dataset Parameters durations: an array, list, pd.DataFrame or pd.Series length n -- duration subject was observed for event_observed: an array, list, pd.DataFrame, or pd.Series, optional True if the the death was observed, False if the event was lost (right-censored). Defaults all True if event_observed==None timeline: an array, list, pd.DataFrame, or pd.Series, optional return the best estimate at the values in timelines (postively increasing) entry: an array, list, pd.DataFrame, or pd.Series, optional relative time when a subject entered the study. This is useful for left-truncated (not left-censored) observations. If None, all members of the population entered study when they were "born". label: string, optional a string to name the column of the estimate. alpha: float, optional the alpha value in the confidence intervals. Overrides the initializing alpha for this call to fit only. left_censorship: bool, optional (default=False) Deprecated, use ``fit_left_censoring`` ci_labels: tuple, optional add custom column names to the generated confidence intervals as a length-2 list: [<lower-bound name>, <upper-bound name>]. Default: <label>_lower_<1-alpha/2> weights: an array, list, pd.DataFrame, or pd.Series, optional if providing a weighted dataset. For example, instead of providing every subject as a single element of `durations` and `event_observed`, one could weigh subject differently. Returns ------- self: KaplanMeierFitter self with new properties like ``survival_function_``, ``plot()``, ``median`` """

        if left_censorship:
            warnings.warn(
                "kwarg left_censorship is deprecated and will be removed in a future release. Please use ``.fit_left_censoring`` instead.",
                DeprecationWarning,
            )

        self._censoring_type = CensoringType.RIGHT
        return self._fit(durations, event_observed, timeline, entry, label, alpha, ci_labels, weights)