code: def predict(self, X, groups=None):
      if self.model is None:
          print("Please train the model first or load model from file!")
          raise ValueError
      sparse = sp.isspmatrix(X)
      if sparse is False:
          X = sp.csr_matrix(X)
      X.data = np.asarray(X.data, dtype=np.float32, order="C")
      X.sort_indices()
      data = X.data.ctypes.data_as(POINTER(c_float))
      indices = X.indices.ctypes.data_as(POINTER(c_int32))
      indptr = X.indptr.ctypes.data_as(POINTER(c_int32))
      if self.objective != "multi:softprob":
          self.predict_label_ptr = (c_float * X.shape[0])()
      else:
          temp_size = X.shape[0] * self.num_class
          self.predict_label_ptr = (c_float * temp_size)()
      if self.group_label is not None:
          group_label = (c_float * len(self.group_label))()
          group_label[:] = self.group_label
      else:
          group_label = None
      in_groups, num_groups = self._construct_groups(groups)
      thundergbm.sparse_predict_scikit(
          X.shape[0],
          data,
          indptr,
          indices,
          self.predict_label_ptr,
          byref(self.model),
          self.n_trees,
          self.tree_per_iter,
          self.objective.encode("utf-8"),
          self.num_class,
          c_float(self.learning_rate),
          group_label,
          in_groups,
          num_groups,
          self.verbose,
      )
      if self.objective == "multi:softprob":
          self.predict_label = np.asarray(self.predict_label_ptr)
          self.predict_label = self.predict_label.reshape(
              [-1, self.num_class]
          )
      else:
          predict_label = [
              self.predict_label_ptr[index]
              for index in range(0, X.shape[0])
          ]
          self.predict_label = np.asarray(predict_label)
      return self.predict_label
  
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 6 for calling the TGBMModel obj's underlying member method code with example usage: obj.predict(X, groups) and returns: self