code: def _check_params(self):
      if self.split_method not in ("inexact", "exact"):
          raise ValueError(
              'Parameter "split_method" must be '
              '"inexact" or "exact".'
          )
      if (
          not isinstance(self.max_iterations, numbers.Integral)
          or self.max_iterations <= 0
      ):
          raise ValueError(
              'Parameter "max_iterations" must be '
              "non-zero positive integer value."
          )
      if (
          not isinstance(self.max_tree_depth, numbers.Integral)
          or self.max_tree_depth < 0
      ):
          raise ValueError(
              'Parameter "max_tree_depth" must be '
              "positive integer value or zero."
          )
      if self.shrinkage < 0 or self.shrinkage >= 1:
          raise ValueError(
              'Parameter "shrinkage" must be '
              "more or equal to 0 and less than 1."
          )
      if self.min_split_loss < 0:
          raise ValueError(
              'Parameter "min_split_loss" must be '
              "more or equal to zero."
          )
      if self.reg_lambda < 0:
          raise ValueError(
              'Parameter "reg_lambda" must be ' "more or equal to zero."
          )
      if (
          self.observations_per_tree_fraction <= 0
          or self.observations_per_tree_fraction > 1
      ):
          raise ValueError(
              'Parameter "observations_per_tree_fraction" must be '
              "more than 0 and less or equal to 1."
          )
      if (
          not isinstance(self.features_per_node, numbers.Integral)
          or self.features_per_node < 0
      ):
          raise ValueError(
              'Parameter "features_per_node" must be '
              "positive integer value or zero."
          )
      if (
          not isinstance(
              self.min_observations_in_leaf_node, numbers.Integral
          )
          or self.min_observations_in_leaf_node <= 0
      ):
          raise ValueError(
              'Parameter "min_observations_in_leaf_node" must be '
              "non-zero positive integer value."
          )
      if not (isinstance(self.memory_saving_mode, bool)):
          raise ValueError(
              'Parameter "memory_saving_mode" must be ' "boolean value."
          )
      if (
          not isinstance(self.max_bins, numbers.Integral)
          or self.max_bins <= 0
      ):
          raise ValueError(
              'Parameter "max_bins" must be '
              "non-zero positive integer value."
          )
      if (
          not isinstance(self.min_bin_size, numbers.Integral)
          or self.min_bin_size <= 0
      ):
          raise ValueError(
              'Parameter "min_bin_size" must be '
              "non-zero positive integer value."
          )
  
imports: import numbers
  import numpy
  from sklearn import preprocessing
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
  from sklearn.utils import check_random_state
  from sklearn.utils.multiclass import check_classification_targets
  from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
  import daal4py
  from .._utils import control_n_jobs, getFPType, run_with_n_jobs
label_desc: Use image node_id 2 for calling the GBTDAALBase obj's underlying member method code with example usage: obj._check_params() without return types