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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 _matrix_flattened_grid(self, X0TAX0, X0TAX0_i, SNR_grids, XTAcorrX, YTAcorrY_diag, XTAcorrY, X0TAY, XTAX0, n_C, n_V, n_X0, n_grid):
 """ We need to integrate parameters SNR and rho on 2-d discrete grids. This function generates matrices which have only one dimension for these two parameters, with each slice in that dimension corresponding to each combination of the discrete grids of SNR and discrete grids of rho. """

        half_log_det_X0TAX0 = np.reshape(
            np.repeat(self._half_log_det(X0TAX0)[None, :],
                      self.SNR_bins, axis=0), n_grid)
        X0TAX0 = np.reshape(
            np.repeat(X0TAX0[None, :, :, :],
                      self.SNR_bins, axis=0),
            (n_grid, n_X0, n_X0))
        X0TAX0_i = np.reshape(np.repeat(
            X0TAX0_i[None, :, :, :],
            self.SNR_bins, axis=0),
            (n_grid, n_X0, n_X0))
        s2XTAcorrX = np.reshape(
            SNR_grids[:, None, None, None]**2 * XTAcorrX,
            (n_grid, n_C, n_C))
        YTAcorrY_diag = np.reshape(np.repeat(
            YTAcorrY_diag[None, :, :],
            self.SNR_bins, axis=0), (n_grid, n_V))
        sXTAcorrY = np.reshape(SNR_grids[:, None, None, None]
                               * XTAcorrY, (n_grid, n_C, n_V))
        X0TAY = np.reshape(np.repeat(X0TAY[None, :, :, :],
                                     self.SNR_bins, axis=0),
                           (n_grid, n_X0, n_V))
        XTAX0 = np.reshape(np.repeat(XTAX0[None, :, :, :],
                                     self.SNR_bins, axis=0),
                           (n_grid, n_C, n_X0))
        return half_log_det_X0TAX0, X0TAX0, X0TAX0_i, s2XTAcorrX, \
            YTAcorrY_diag, sXTAcorrY, X0TAY, XTAX0