code: def __init__(
      self,
      split_method="inexact",
      max_iterations=50,
      max_tree_depth=6,
      shrinkage=0.3,
      min_split_loss=0,
      reg_lambda=1,
      observations_per_tree_fraction=1,
      features_per_node=0,
      min_observations_in_leaf_node=5,
      memory_saving_mode=False,
      max_bins=256,
      min_bin_size=5,
      random_state=None,
  ):
      self.split_method = split_method
      self.max_iterations = max_iterations
      self.max_tree_depth = max_tree_depth
      self.shrinkage = shrinkage
      self.min_split_loss = min_split_loss
      self.reg_lambda = reg_lambda
      self.observations_per_tree_fraction = (
          observations_per_tree_fraction
      )
      self.features_per_node = features_per_node
      self.min_observations_in_leaf_node = min_observations_in_leaf_node
      self.memory_saving_mode = memory_saving_mode
      self.max_bins = max_bins
      self.min_bin_size = min_bin_size
      self.random_state = random_state
  
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 1 to create a new GBTDAALBase object from inherited base classes: BaseEstimator, d4p.mb with example: obj = GBTDAALBase(split_method, max_iterations, max_tree_depth, shrinkage, min_split_loss, reg_lambda, observations_per_tree_fraction, features_per_node, min_observations_in_leaf_node, memory_saving_mode, max_bins, min_bin_size, random_state)