code: def fit_left_censoring(
          self,
          durations,
          event_observed=None,
          timeline=None,
          entry=None,
          label="KM_estimate",
          alpha=None,
          ci_labels=None,
          weights=None,
      ):
          
          self._censoring_type = CensoringType.LEFT
          return self._fit(durations, event_observed, timeline, entry, label, alpha, ci_labels, weights)
docs: Fit the model to a left-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``