def fit(
        self,
        durations,
        event_observed=None,
        timeline=None,
        label=None,
        alpha=None,
        ci_labels=None,
        show_progress=False,
        entry=None,
        weights=None,
        left_censorship=False,
    ):  # pylint: disable=too-many-arguments
        """
        Parameters
        ----------
        durations: an array, or pd.Series
          length n, duration subject was observed for
        event_observed: numpy array or pd.Series, optional
          length n, True if the the death was observed, False if the event was lost (right-censored). Defaults all True if event_observed==None
        timeline: list, optional
            return the estimate at the values in timeline (positively increasing)
        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.
        ci_labels: list, optional
            add custom column names to the generated confidence intervals as a length-2 list: [<lower-bound name>, <upper-bound name>]. Default: <label>_lower_<alpha>
        show_progress: boolean, optional
            since this is an iterative fitting algorithm, switching this to True will display some iteration details.
        entry: an array, or pd.Series, of length n
            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": time zero.
        weights: an array, or pd.Series, of length n
            integer weights per observation
        Returns
        -------
          self
            self with new properties like ``cumulative_hazard_``, ``survival_function_``

        """
        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,
            )
            return self.fit_left_censoring(
                durations, event_observed, timeline, label, alpha, ci_labels, show_progress, entry, weights
            )

        self.durations = np.asarray(pass_for_numeric_dtypes_or_raise_array(durations))
        check_nans_or_infs(self.durations)
        check_positivity(self.durations)
        self._censoring_type = CensoringType.RIGHT

        return self._fit(
            (self.durations, None),
            event_observed=event_observed,
            timeline=timeline,
            label=label,
            alpha=alpha,
            ci_labels=ci_labels,
            show_progress=show_progress,
            entry=entry,
            weights=weights,
        )
Parameters
        ----------
        durations: an array, or pd.Series
          length n, duration subject was observed for
        event_observed: numpy array or pd.Series, optional
          length n, True if the the death was observed, False if the event was lost (right-censored). Defaults all True if event_observed==None
        timeline: list, optional
            return the estimate at the values in timeline (positively increasing)
        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.
        ci_labels: list, optional
            add custom column names to the generated confidence intervals as a length-2 list: [<lower-bound name>, <upper-bound name>]. Default: <label>_lower_<alpha>
        show_progress: boolean, optional
            since this is an iterative fitting algorithm, switching this to True will display some iteration details.
        entry: an array, or pd.Series, of length n
            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": time zero.
        weights: an array, or pd.Series, of length n
            integer weights per observation
        Returns
        -------
          self
            self with new properties like ``cumulative_hazard_``, ``survival_function_``