code: def __init__(
      self,
      model_type=None,
      data=None,
      label=None,
      sample_weight=None,
      init_score=None,
      eval_datas=None,
      eval_labels=None,
      eval_sample_weights=None,
      eval_init_scores=None,
      params=None,
      kwds=None,
      workers=None,
      worker_id=None,
      worker_ports=None,
      tree_learner=None,
      timeout=None,
      **kw,
  ):
      super().__init__(
          _model_type=model_type,
          _params=params,
          _data=data,
          _label=label,
          _sample_weight=sample_weight,
          _init_score=init_score,
          _eval_datas=eval_datas,
          _eval_labels=eval_labels,
          _eval_sample_weights=eval_sample_weights,
          _eval_init_scores=eval_init_scores,
          _kwds=kwds,
          _workers=workers,
          _worker_id=worker_id,
          _worker_ports=worker_ports,
          _tree_learner=tree_learner,
          _timeout=timeout,
          **kw,
      )
      if self.output_types is None:
          self.output_types = [OutputType.object]
  
imports: import itertools
  import logging
  import operator
  import pickle
  from collections import defaultdict
  from functools import reduce
  import numpy
  from ....None import opcodes
  from ....core import ExecutableTuple, OutputType, recursive_tile
  from ....core.context import get_context
  from ....core.operand import MergeDictOperand
  from ....serialization.serializables import FieldTypes, DictField, Int32Field, KeyField, ListField, StringField
  from ...utils import concat_chunks, collect_ports
  from ._align import align_data_set
  from .core import LGBMModelType, get_model_cls_from_type
label_desc: Use image node_id 1 to create a new LGBMTrain object from inherited base classes: MergeDictOperand with example: obj = LGBMTrain(model_type, data, label, sample_weight, init_score, eval_datas, eval_labels, eval_sample_weights, eval_init_scores, params, kwds, workers, worker_id, worker_ports, tree_learner, timeout)