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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 compute(self, rtdc_ds):
 """Compute the feature with self.method Parameters rtdc_ds: instance of RTDCBase The dataset to compute the feature for Returns ------- feature: array- or list-like The computed data feature (read-only). """

        data = self.method(rtdc_ds)
        dsize = len(rtdc_ds) - len(data)

        if dsize > 0:
            msg = "Growing feature {} in {} by {} to match event number!"
            warnings.warn(msg.format(self.feature_name, rtdc_ds, abs(dsize)),
                          BadFeatureSizeWarning)
            data.resize(len(rtdc_ds), refcheck=False)
            data[-dsize:] = np.nan
        elif dsize < 0:
            msg = "Shrinking feature {} in {} by {} to match event number!"
            warnings.warn(msg.format(self.feature_name, rtdc_ds, abs(dsize)),
                          BadFeatureSizeWarning)
            data.resize(len(rtdc_ds), refcheck=False)

        if isinstance(data, np.ndarray):
            data.setflags(write=False)
        elif isinstance(data, list):
            for item in data:
                if isinstance(item, np.ndarray):
                    item.setflags(write=False)

        return data