code: def tile(cls, op: "StatsModelsPredict"):
      exog = op.inputs[0]
      out = op.outputs[0]
  
      if exog.ndim > 1 and exog.chunk_shape[1] > 1:
          exog = yield from recursive_tile(
              exog.rechunk({1: exog.shape[1]})
          )
  
      chunks = []
      for in_chunk in exog.chunks:
          if isinstance(exog, (DATAFRAME_TYPE, SERIES_TYPE)):
              kwargs = dict(
                  index=in_chunk.index[:1],
                  shape=in_chunk.shape[:1],
                  index_value=in_chunk.index_value,
                  dtype=out.dtype,
              )
          else:
              kwargs = dict(
                  index=in_chunk.index[:1],
                  shape=in_chunk.shape[:1],
                  dtype=out.dtype,
              )
  
          new_op = op.copy().reset_key()
          chunks.append(new_op.new_chunk([in_chunk], **kwargs))
  
      new_op = op.copy().reset_key()
      return new_op.new_tileables(
          [exog],
          chunks=chunks,
          nsplits=(exog.nsplits[0],),
          **out.params,
      )
  
file_path: mars/mars/learn/contrib/statsmodels/predict.py
imports: import pickle
  import numpy
  from ....None import opcodes
  from ....core import OutputType, recursive_tile
  from ....dataframe.core import DATAFRAME_TYPE, SERIES_TYPE
  from ....serialization.serializables import BytesField, DictField, TupleField
  from ...operands import LearnOperand, LearnOperandMixin
label_desc: Use image node_id 6 for calling the StatsModelsPredict obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op