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 hasattr(self, "min_impurity_split"):
          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 getattr(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 ABC
  import numpy
  from scipy import sparse
  from sklearn.base import clone
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.ensemble import ExtraTreesRegressor
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.ensemble._forest import _get_n_samples_bootstrap
  from sklearn.exceptions import DataConversionWarning
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, ExtraTreeClassifier, ExtraTreeRegressor
  from sklearn.tree._tree import Tree
  from sklearn.utils import check_random_state, deprecated
  from sklearn.utils.validation import check_array, check_consistent_length, check_is_fitted, check_X_y
  from daal4py.sklearn._utils import check_tree_nodes, control_n_jobs, daal_check_version, run_with_n_jobs, sklearn_check_version
  from onedal.ensemble import ExtraTreesClassifier
  from onedal.ensemble import ExtraTreesRegressor
  from onedal.ensemble import RandomForestClassifier
  from onedal.ensemble import RandomForestRegressor
  from onedal.primitives import get_tree_state_cls, get_tree_state_reg
  from onedal.utils import _num_features, _num_samples
  from .._config import get_config
  from .._device_offload import dispatch, wrap_output_data
  from .._utils import PatchingConditionsChain
label_desc: Use image node_id 4 for calling the BaseForest obj's underlying member method code with example usage: obj._check_parameters() without return types