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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 evaluate(self, x, y, flux, x_0, y_0, use_oversampling=True):
 """ Evaluate the model on some input variables and provided model parameters. Parameters use_oversampling : bool, optional Whether to use the oversampling factor to calculate the model pixel indices. The default is `True`, which means the input indices will be multipled by this factor. """


        if use_oversampling:
            xi = self._oversampling[0] * (np.asarray(x) - x_0)
            yi = self._oversampling[1] * (np.asarray(y) - y_0)
        else:
            xi = np.asarray(x) - x_0
            yi = np.asarray(y) - y_0

        xi += self._x_origin
        yi += self._y_origin

        f = flux * self._normalization_constant
        evaluated_model = f * self.interpolator.ev(xi, yi)

        if self._fill_value is not None:
            # find indices of pixels that are outside the input pixel grid and
            # set these pixels to the 'fill_value':
            invalid = (((xi < 0) | (xi > self._nx - 1)) |
                       ((yi < 0) | (yi > self._ny - 1)))
            evaluated_model[invalid] = self._fill_value

        return evaluated_model