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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 get_samples(self, init_points_count):
 """ This method may return less points than requested. The total number of generated points is the smallest closest integer of n^d to the selected amount of points. """


        init_points_count = self._adjust_init_points_count(init_points_count)
        samples = np.empty((init_points_count, self.space.dimensionality))

        # Use random design to fill non-continuous variables
        random_design = RandomDesign(self.space)
        random_design.fill_noncontinous_variables(samples)

        if self.space.has_continuous():
            X_design = multigrid(self.space.get_continuous_bounds(), self.data_per_dimension)
            samples[:,self.space.get_continuous_dims()] = X_design

        return samples