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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=None, event_col=None, show_progress=False, initial_point=None, strata=None, step_size=None, weights_col=None, cluster_col=None, robust=False, batch_mode=None, ):
 """ Fit the Cox proportional hazard model to a dataset. Parameters df: DataFrame a Pandas DataFrame with necessary columns `duration_col` and `event_col` (see below), covariates columns, and special columns (weights, strata). `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 thecolumn in DataFrame that contains the subjects' death observation. If left as None, assume all individuals are uncensored. weights_col: string, optional an optional column in the DataFrame, df, that denotes the weight per subject. This column is expelled and not used as a covariate, but as a weight in the final regression. Default weight is 1. This can be used for case-weights. For example, a weight of 2 means there were two subjects with identical observations. This can be used for sampling weights. In that case, use `robust=True` to get more accurate standard errors. show_progress: boolean, optional (default=False) since the fitter is iterative, show convergence diagnostics. Useful if convergence is failing. initial_point: (d,) numpy array, optional initialize the starting point of the iterative algorithm. Default is the zero vector. strata: list or string, optional specify a column or list of columns n to use in stratification. This is useful if a categorical covariate does not obey the proportional hazard assumption. This is used similar to the `strata` expression in R. See http://courses.washington.edu/b515/l17.pdf. step_size: float, optional set an initial step size for the fitting algorithm. Setting to 1.0 may improve performance, but could also hurt convergence. robust: boolean, optional (default=False) Compute the robust errors using the Huber sandwich estimator, aka Wei-Lin estimate. This does not handle ties, so if there are high number of ties, results may significantly differ. See "The Robust Inference for the Cox Proportional Hazards Model", Journal of the American Statistical Association, Vol. 84, No. 408 (Dec., 1989), pp. 1074- 1078 cluster_col: string, optional specifies what column has unique identifiers for clustering covariances. Using this forces the sandwich estimator (robust variance estimator) to be used. batch_mode: bool, optional enabling batch_mode can be faster for datasets with a large number of ties. If left as None, lifelines will choose the best option. Returns ------- self: CoxPHFitter self with additional new properties: ``print_summary``, ``hazards_``, ``confidence_intervals_``, ``baseline_survival_``, etc. Note ---- Tied survival times are handled using Efron's tie-method. Examples -------- """

        if duration_col is None:
            raise TypeError("duration_col cannot be None.")

        self._censoring_type = CensoringType.RIGHT
        self._time_fit_was_called = datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S") + " UTC"
        self.duration_col = duration_col
        self.event_col = event_col
        self.robust = robust
        self.cluster_col = cluster_col
        self.weights_col = weights_col
        self._n_examples = df.shape[0]
        self._batch_mode = batch_mode
        self.strata = coalesce(strata, self.strata)

        X, T, E, weights, original_index, self._clusters = self._preprocess_dataframe(df)

        self.durations = T.copy()
        self.event_observed = E.copy()
        self.weights = weights.copy()

        if self.strata is not None:
            self.durations.index = original_index
            self.event_observed.index = original_index
            self.weights.index = original_index

        self._norm_mean = X.mean(0)
        self._norm_std = X.std(0)
        X_norm = normalize(X, self._norm_mean, self._norm_std)

        hazards_ = self._fit_model(
            X_norm, T, E, weights=weights, initial_point=initial_point, show_progress=show_progress, step_size=step_size
        )

        self.hazards_ = pd.Series(hazards_, index=X.columns, name="coef") / self._norm_std

        self.variance_matrix_ = -inv(self._hessian_) / np.outer(self._norm_std, self._norm_std)
        self.standard_errors_ = self._compute_standard_errors(X_norm, T, E, weights)
        self.confidence_intervals_ = self._compute_confidence_intervals()

        self._predicted_partial_hazards_ = (
            self.predict_partial_hazard(X)
            .rename(columns={0: "P"})
            .assign(T=self.durations.values, E=self.event_observed.values, W=self.weights.values)
            .set_index(X.index)
        )
        self.baseline_hazard_ = self._compute_baseline_hazards()
        self.baseline_cumulative_hazard_ = self._compute_baseline_cumulative_hazard()
        self.baseline_survival_ = self._compute_baseline_survival()

        if hasattr(self, "_concordance_score_"):
            # we have already fit the model.
            del self._concordance_score_

        return self