How to use the lifelines.utils.check_for_numeric_dtypes_or_raise function in lifelines

same order as the training data.

        Returns
        -------
        DataFrame

        Note
        -----
        If X is a DataFrame, the order of the columns do not matter. But
        if X is an array, then the column ordering is assumed to be the
        same as the training dataset.
        """
        if isinstance(X, pd.DataFrame):
            order = self.params_.index
            X = X[order]
            check_for_numeric_dtypes_or_raise(X)

        X = X.astype(float)
        index = _get_index(X)
        X = normalize(X, self._norm_mean.values, 1)
        return pd.DataFrame(np.dot(X, self.params_), index=index)

def _check_values_pre_fitting(self, X, T, E, W):
        check_low_var(X)
        check_for_numeric_dtypes_or_raise(X)
        check_nans_or_infs(T)
        check_nans_or_infs(X)
        # check to make sure their weights are okay
        if self.weights_col:
            if (W.astype(int) != W).any() and not self.robust:
                warnings.warn(
                    """It appears your weights are not integers, possibly propensity or sampling scores then?
It's important to know that the naive variance estimates of the coefficients are biased. Instead a) set `robust=True` in the call to `fit`, or b) use Monte Carlo to
estimate the variances. See paper "Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis"
""",
                    StatisticalWarning,
                )
            if (W <= 0).any():
                raise ValueError("values in weight column %s must be positive." % self.weights_col)

def _check_values_pre_fitting(self, df, T, E, weights, entries):
        utils.check_for_numeric_dtypes_or_raise(df)
        utils.check_nans_or_infs(df)
        utils.check_nans_or_infs(T)
        utils.check_nans_or_infs(E)
        utils.check_positivity(T)

        if self.weights_col:
            if (weights.astype(int) != weights).any() and not self.robust:
                warnings.warn(
                    dedent(
                        """It appears your weights are not integers, possibly propensity or sampling scores then?
                                        It's important to know that the naive variance estimates of the coefficients are biased. Instead a) set `robust=True` in the call to `fit`, or b) use Monte Carlo to
                                        estimate the variances. See paper "Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis"""
                    ),
                    utils.StatisticalWarning,
                )
            if (weights <= 0).any():

def _check_values(self, df, events, start, stop):
        # check_for_overlapping_intervals(df) # this is currently too slow for production.
        check_nans_or_infs(df)
        check_low_var(df)
        check_complete_separation_low_variance(df, events, self.event_col)
        check_for_numeric_dtypes_or_raise(df)
        check_for_immediate_deaths(events, start, stop)
        check_for_instantaneous_events_at_time_zero(start, stop)
        check_for_instantaneous_events_at_death_time(events, start, stop)