code: def cv(self, X, y, folds=None, nfold=5, shuffle=True, seed=0):
      if self._impl == "ranker":
          print(
              "Cross-validation for ## Ranker ## have not been supported yep.."
          )
          return
  
      sparse = sp.isspmatrix(X)
      if sparse is False:
          X = sp.csr_matrix(X)
      X, y = check_X_y(
          X, y, dtype=np.float64, order="C", accept_sparse="csr"
      )
      y = np.asarray(y, dtype=np.float32, order="C")
      n_instances = X.shape[0]
      if folds is not None:
          # use specified validation set
          train_idset = [x[0] for x in folds]
          test_idset = [x[1] for x in folds]
      else:
          if shuffle:
              randidx = np.random.RandomState(seed).permutation(
                  n_instances
              )
          else:
              randidx = np.arange(n_instances)
          kstep = int(n_instances / nfold)
          test_idset = [
              randidx[i : i + kstep]
              for i in range(0, n_instances, kstep)
          ]
          train_idset = [
              np.concatenate(
                  [test_idset[i] for i in range(nfold) if k != i]
              )
              for k in range(nfold)
          ]
      # to be optimized: get score in fit; early stopping; more metrics;
      train_score_list = []
      test_score_list = []
      for k in range(nfold):
          X_train = X[train_idset[k], :]
          X_test = X[test_idset[k], :]
          y_train = y[train_idset[k]]
          y_test = y[test_idset[k]]
          self.fit(X_train, y_train)
          y_train_pred = self.predict(X_train)
          y_test_pred = self.predict(X_test)
          if self._impl == "classifier":
              train_score = accuracy_score(y_train, y_train_pred)
              test_score = accuracy_score(y_test, y_test_pred)
              train_score_list.append(train_score)
              test_score_list.append(test_score)
          elif self._impl == "regressor":
              train_score = mean_squared_error(y_train, y_train_pred)
              test_score = mean_squared_error(y_test, y_test_pred)
              train_score_list.append(train_score)
              test_score_list.append(test_score)
      self.eval_res = {}
      if self._impl == "classifier":
          self.eval_res["train-accuracy-mean"] = statistics.mean(
              train_score_list
          )
          self.eval_res["train-accuracy-std"] = statistics.stdev(
              train_score_list
          )
          self.eval_res["test-accuracy-mean"] = statistics.mean(
              test_score_list
          )
          self.eval_res["test-accuracy-std"] = statistics.stdev(
              test_score_list
          )
          print(
              "mean train accuracy:%.6f+%.6f"
              % (
                  statistics.mean(train_score_list),
                  statistics.stdev(train_score_list),
              )
          )
          print(
              "mean test accuracy:%.6f+%.6f"
              % (
                  statistics.mean(test_score_list),
                  statistics.stdev(test_score_list),
              )
          )
      elif self._impl == "regressor":
          self.eval_res["train-RMSE-mean"] = statistics.mean(
              train_score_list
          )
          self.eval_res["train-RMSE-std"] = statistics.stdev(
              train_score_list
          )
          self.eval_res["test-RMSE-mean"] = statistics.mean(
              test_score_list
          )
          self.eval_res["test-RMSE-std"] = statistics.stdev(
              test_score_list
          )
          print(
              "mean train RMSE:%.6f+%.6f"
              % (
                  statistics.mean(train_score_list),
                  statistics.stdev(train_score_list),
              )
          )
          print(
              "mean test RMSE:%.6f+%.6f"
              % (
                  statistics.mean(test_score_list),
                  statistics.stdev(test_score_list),
              )
          )
      return self.eval_res
  
imports: from sklearn.base import BaseEstimator
  from sklearn.base import RegressorMixin, ClassifierMixin
  from sklearn.metrics import mean_squared_error, accuracy_score
  import numpy
  import scipy.sparse
  import statistics
  from sklearn.utils import check_X_y
  from ctypes import *
  from os import path
  from sys import platform
label_desc: Use image node_id 9 for calling the TGBMModel obj's underlying member method code with example usage: obj.cv(X, y, folds, nfold, shuffle, seed) and returns: self