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``

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)