code: def predict(
      model, data, session=None, run_kwargs=None, run=True, **kw
  ):
      if hasattr(model, "classes_"):
          return predict_base(
              model,
              data,
              session=session,
              run_kwargs=run_kwargs,
              proba=False,
              run=run,
              **kw,
          )
      else:
          return predict_base(
              model,
              data,
              session=session,
              run_kwargs=run_kwargs,
              run=run,
              **kw,
          )
  
imports: import pickle
  import numpy
  import pandas
  from ....None import opcodes
  from ....core import recursive_tile
  from ....dataframe.utils import parse_index
  from ....serialization.serializables import BoolField, BytesField, DictField, KeyField
  from ....tensor.core import TENSOR_TYPE, TensorOrder
  from ...operands import LearnOperand, LearnOperandMixin, OutputType
label_desc: Use image node_id 2 for calling a global function with example usage: predict(model, data, session, run_kwargs, run) and returns: predict_base, predict_base