code: def _check_parameters(self):
      if isinstance(self.min_samples_leaf, numbers.Integral):
          if not 1 <= self.min_samples_leaf:
              raise ValueError(
                  "min_samples_leaf must be at least 1 "
                  "or in (0, 0.5], got %s" % self.min_samples_leaf
              )
      else:  # float
          if not 0.0 < self.min_samples_leaf <= 0.5:
              raise ValueError(
                  "min_samples_leaf must be at least 1 "
                  "or in (0, 0.5], got %s" % self.min_samples_leaf
              )
      if isinstance(self.min_samples_split, numbers.Integral):
          if not 2 <= self.min_samples_split:
              raise ValueError(
                  "min_samples_split must be an integer "
                  "greater than 1 or a float in (0.0, 1.0]; "
                  "got the integer %s" % self.min_samples_split
              )
      else:  # float
          if not 0.0 < self.min_samples_split <= 1.0:
              raise ValueError(
                  "min_samples_split must be an integer "
                  "greater than 1 or a float in (0.0, 1.0]; "
                  "got the float %s" % self.min_samples_split
              )
      if not 0 <= self.min_weight_fraction_leaf <= 0.5:
          raise ValueError("min_weight_fraction_leaf must in [0, 0.5]")
      if self.min_impurity_split is not None:
          warnings.warn(
              "The min_impurity_split parameter is deprecated. "
              "Its default value has changed from 1e-7 to 0 in "
              "version 0.23, and it will be removed in 0.25. "
              "Use the min_impurity_decrease parameter instead.",
              FutureWarning,
          )
  
          if self.min_impurity_split < 0.0:
              raise ValueError(
                  "min_impurity_split must be greater than "
                  "or equal to 0"
              )
      if self.min_impurity_decrease < 0.0:
          raise ValueError(
              "min_impurity_decrease must be greater than "
              "or equal to 0"
          )
      if self.max_leaf_nodes is not None:
          if not isinstance(self.max_leaf_nodes, numbers.Integral):
              raise ValueError(
                  "max_leaf_nodes must be integral number but was "
                  "%r" % self.max_leaf_nodes
              )
          if self.max_leaf_nodes < 2:
              raise ValueError(
                  (
                      "max_leaf_nodes {0} must be either None "
                      "or larger than 1"
                  ).format(self.max_leaf_nodes)
              )
      if isinstance(self.max_bins, numbers.Integral):
          if not 2 <= self.max_bins:
              raise ValueError(
                  "max_bins must be at least 2, got %s" % self.max_bins
              )
      else:
          raise ValueError(
              "max_bins must be integral number but was "
              "%r" % self.max_bins
          )
      if isinstance(self.min_bin_size, numbers.Integral):
          if not 1 <= self.min_bin_size:
              raise ValueError(
                  "min_bin_size must be at least 1, got %s"
                  % self.min_bin_size
              )
      else:
          raise ValueError(
              "min_bin_size must be integral number but was "
              "%r" % self.min_bin_size
          )
  
imports: import numbers
  import warnings
  from abc import ABCMeta, abstractmethod
  from math import ceil
  from numbers import Number
  import numpy
  from scipy import sparse
  from sklearn.ensemble import BaseEnsemble
  from sklearn.exceptions import DataConversionWarning
  from sklearn.utils import check_array, check_random_state, compute_sample_weight, deprecated
  from sklearn.utils.validation import _num_samples, check_consistent_length, check_is_fitted
  from daal4py.sklearn._utils import daal_check_version, sklearn_check_version
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted
  from ..common._mixin import ClassifierMixin, RegressorMixin
  from ..common._policy import _get_policy
  from ..datatypes import _convert_to_supported, from_table, to_table
  from ..utils import _check_array, _check_n_features, _check_X_y, _column_or_1d, _validate_targets
label_desc: Use image node_id 5 for calling the BaseForest obj's underlying member method code with example usage: obj._check_parameters() without return types