How to use the econml.dgp function in econml

beta[beta_support] = np.random.normal(size=len(beta_support))
            beta = beta / np.linalg.norm(beta)

            # DGP. Create samples of data (y, T, X) from known truth
            y, T, X, _ = econml.dgp.dgp_data_multiple_treatments(
                n_samples, n_cov, n_treatments, alpha, beta, effect)

            # DML Estimation.
            dml_reg = econml.dml.DML(np.arange(X.shape[1]), [], np.arange(X.shape[1], X.shape[1] + T.shape[1]),
                                     model_y=internal_reg_y,
                                     model_t=internal_reg_t,
                                     model_f=internal_reg_f,
                                     model_c=internal_reg_c)
            dml_reg.fit(np.concatenate((X, T), axis=1), y)

            y_test, T_test, X_test = econml.dgp.dgp_counterfactual_data_multiple_treatments(
                n_samples, n_cov, beta, effect, 5. * np.ones(n_treatments))
            dml_r2score.append(dml_reg.score(np.concatenate((X_test, T_test), axis=1), y_test))
            dml_te.append(dml_reg.effect(np.zeros((1, 1)), np.ones((1, 1)), np.zeros((1, 0))))

            # Estimation with other methods for comparison
            direct_reg1.fit(np.concatenate((X, T), axis=1), y)
            direct_r2score1.append(direct_reg1.score(np.concatenate((X_test, T_test), axis=1), y_test))
            direct_te1.append(direct_reg1.coef_[X.shape[1]])

            direct_reg2.fit(np.concatenate((X, T), axis=1), y)
            direct_r2score2.append(direct_reg2.score(np.concatenate((X_test, T_test), axis=1), y_test))
            direct_te2.append(direct_reg2.coef_[X.shape[1]])

        dml_r2scores.append(dml_r2score)
        direct_r2scores1.append(direct_r2score1)
        direct_r2scores2.append(direct_r2score2)

# Sparse coefficients of treatment as a function of co-variates
            alpha_sparsity = sparsity
            alpha_support = np.random.choice(n_cov, alpha_sparsity, replace=False)
            alpha = np.zeros(n_cov)
            alpha[alpha_support] = np.random.normal(size=len(alpha_support))
            alpha = alpha.reshape((-1, 1))
            alpha = alpha / np.linalg.norm(alpha)
            # Coefficients of outcomes as a function of co-variates
            beta_sparsity = sparsity
            beta_support = np.random.choice(n_cov, beta_sparsity, replace=False)
            beta = np.zeros(n_cov)
            beta[beta_support] = np.random.normal(size=len(beta_support))
            beta = beta / np.linalg.norm(beta)

            # DGP. Create samples of data (y, T, X) from known truth
            y, T, X, _ = econml.dgp.dgp_data_multiple_treatments(
                n_samples, n_cov, n_treatments, alpha, beta, effect)

            # DML Estimation.
            dml_reg = econml.dml.DML(np.arange(X.shape[1]), [], np.arange(X.shape[1], X.shape[1] + T.shape[1]),
                                     model_y=internal_reg_y,
                                     model_t=internal_reg_t,
                                     model_f=internal_reg_f,
                                     model_c=internal_reg_c)
            dml_reg.fit(np.concatenate((X, T), axis=1), y)

            y_test, T_test, X_test = econml.dgp.dgp_counterfactual_data_multiple_treatments(
                n_samples, n_cov, beta, effect, 5. * np.ones(n_treatments))
            dml_r2score.append(dml_reg.score(np.concatenate((X_test, T_test), axis=1), y_test))
            dml_te.append(dml_reg.effect(np.zeros((1, 1)), np.ones((1, 1)), np.zeros((1, 0))))

            # Estimation with other methods for comparison