code: def __call__(self):
      num_class = self.model.attr("num_class")
      if num_class is not None:
          num_class = int(num_class)
      if num_class is not None:
          shape = (self.data.shape[0], num_class)
      else:
          shape = (self.data.shape[0],)
      inputs = [self.data]
      if self.output_types[0] == OutputType.tensor:
          # tensor
          return self.new_tileable(
              inputs,
              shape=shape,
              dtype=np.dtype(np.float32),
              order=TensorOrder.C_ORDER,
          )
      elif self.output_types[0] == OutputType.dataframe:
          # dataframe
          dtypes = pd.DataFrame(
              np.random.rand(0, num_class), dtype=np.float32
          ).dtypes
          return self.new_tileable(
              inputs,
              shape=shape,
              dtypes=dtypes,
              columns_value=parse_index(dtypes.index),
              index_value=self.data.index_value,
          )
      else:
          # series
          return self.new_tileable(
              inputs,
              shape=shape,
              index_value=self.data.index_value,
              name="predictions",
              dtype=np.dtype(np.float32),
          )
  
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 3 for calling the XGBPredict obj's underlying member method code with example usage: obj.__call__() and returns: self, self, self