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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, df, duration_col, event_col=None, ancillary_df=None, show_progress=False, timeline=None, weights_col=None, robust=False, initial_point=None, entry_col=None, ):
 """ Fit the accelerated failure time model to a right-censored dataset. Parameters df: DataFrame a Pandas DataFrame with necessary columns `duration_col` and `event_col` (see below), covariates columns, and special columns (weights). `duration_col` refers to the lifetimes of the subjects. `event_col` refers to whether the 'death' events was observed: 1 if observed, 0 else (censored). duration_col: string the name of the column in DataFrame that contains the subjects' lifetimes. event_col: string, optional the name of the column in DataFrame that contains the subjects' death observation. If left as None, assume all individuals are uncensored. show_progress: boolean, optional (default=False) since the fitter is iterative, show convergence diagnostics. Useful if convergence is failing. ancillary_df: None, boolean, or DataFrame, optional (default=None) Choose to model the ancillary parameters. If None or False, explicitly do not fit the ancillary parameters using any covariates. If True, model the ancillary parameters with the same covariates as ``df``. If DataFrame, provide covariates to model the ancillary parameters. Must be the same row count as ``df``. timeline: array, optional Specify a timeline that will be used for plotting and prediction weights_col: string the column in DataFrame that specifies weights per observation. robust: boolean, optional (default=False) Compute the robust errors using the Huber sandwich estimator. initial_point: (d,) numpy array, optional initialize the starting point of the iterative algorithm. Default is the zero vector. entry_col: specify a column in the DataFrame that denotes any late-entries (left truncation) that occurred. See the docs on `left truncation <https://lifelines.readthedocs.io/en/latest/Survival%20analysis%20with%20lifelines.html#left-truncated-late-entry-data>`__ Returns ------- self: self with additional new properties: ``print_summary``, ``params_``, ``confidence_intervals_`` and more Examples -------- """

        self.duration_col = duration_col
        self._time_cols = [duration_col]
        self._censoring_type = CensoringType.RIGHT

        df = df.copy()

        T = pass_for_numeric_dtypes_or_raise_array(df.pop(duration_col)).astype(float)
        self.durations = T.copy()

        self._fit(
            self._log_likelihood_right_censoring,
            df,
            (T.values, None),
            event_col=event_col,
            ancillary_df=ancillary_df,
            show_progress=show_progress,
            timeline=timeline,
            weights_col=weights_col,
            robust=robust,
            initial_point=initial_point,
            entry_col=entry_col,
        )

        return self