code: def __init__(
      self,
      depth=6,
      n_trees=40,
      n_gpus=1,
      min_child_weight=1.0,
      lambda_tgbm=1.0,
      gamma=1.0,
      max_num_bin=255,
      verbose=1,
      column_sampling_rate=1.0,
      bagging=0,
      n_parallel_trees=1,
      learning_rate=1.0,
      objective="multi:softmax",
      num_class=2,
      tree_method="auto",
  ):
      super().__init__(
          depth=depth,
          n_trees=n_trees,
          n_gpus=n_gpus,
          min_child_weight=min_child_weight,
          lambda_tgbm=lambda_tgbm,
          gamma=gamma,
          max_num_bin=max_num_bin,
          verbose=verbose,
          column_sampling_rate=column_sampling_rate,
          bagging=bagging,
          n_parallel_trees=n_parallel_trees,
          learning_rate=learning_rate,
          objective=objective,
          num_class=num_class,
          tree_method=tree_method,
      )
  
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 1 to create a new TGBMClassifier object from inherited base classes: TGBMModel, ThundergbmClassifierBase with example: obj = TGBMClassifier(depth, n_trees, n_gpus, min_child_weight, lambda_tgbm, gamma, max_num_bin, verbose, column_sampling_rate, bagging, n_parallel_trees, learning_rate, objective, num_class, tree_method)