code: def predict(
      model,
      data,
      output_margin=False,
      ntree_limit=None,
      validate_features=True,
      base_margin=None,
      session=None,
      run_kwargs=None,
      run=True,
  ):
      import xgboost
  
      data = check_data(data)
      if not isinstance(model, xgboost.Booster):
          raise TypeError(
              f"model has to be a xgboost.Booster, got {type(model)} instead"
          )
  
      num_class = model.attr("num_class")
      if isinstance(data, TENSOR_TYPE):
          output_types = [OutputType.tensor]
      elif num_class is not None:
          output_types = [OutputType.dataframe]
      else:
          output_types = [OutputType.series]
  
      kwargs = {
          "output_margin": output_margin,
          "ntree_limit": ntree_limit,
          "validate_features": validate_features,
          "base_margin": base_margin,
      }
      op = XGBPredict(
          data=data,
          model=model,
          kwargs=kwargs,
          gpu=data.op.gpu,
          output_types=output_types,
      )
      result = op()
      if run:
          result.execute(session=session, **(run_kwargs or dict()))
      return result
  
imports: import pickle
  import numpy
  import pandas
  from ....None import opcodes
  from ....core import recursive_tile
  from ....serialization.serializables import KeyField, BytesField, DictField, FieldTypes
  from ....dataframe.core import SERIES_CHUNK_TYPE, DATAFRAME_CHUNK_TYPE
  from ....dataframe.utils import parse_index
  from ....tensor.core import TENSOR_TYPE, TensorOrder
  from ....utils import has_unknown_shape, ensure_own_data
  from ...operands import LearnOperand, LearnOperandMixin, OutputType
  from .dmatrix import ToDMatrix, check_data
label_desc: Use image node_id 1 for calling a global function with example usage: predict(model, data, output_margin, ntree_limit, validate_features, base_margin, session, run_kwargs, run) and returns: result