code: def execute(cls, ctx, op: "XGBPredict"):
      from xgboost import DMatrix
  
      raw_data = data = ctx[op.data.key]
      if isinstance(data, tuple):
          data = ToDMatrix.get_xgb_dmatrix(ensure_own_data(data))
      else:
          data = data.spmatrix if hasattr(data, "spmatrix") else data
          data = DMatrix(data)
  
      # do not pass arguments that are None
      kwargs = dict(
          (k, v) for k, v in op.kwargs.items() if v is not None
      )
      result = op.model.predict(data, **kwargs)
  
      if isinstance(op.outputs[0], DATAFRAME_CHUNK_TYPE):
          result = pd.DataFrame(result, index=raw_data.index)
      elif isinstance(op.outputs[0], SERIES_CHUNK_TYPE):
          result = pd.Series(
              result, index=raw_data.index, name="predictions"
          )
  
      ctx[op.outputs[0].key] = 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 5 for calling the XGBPredict obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types