code: def tile(cls, op: "LGBMPredict"):
      out = op.outputs[0]
      out_chunks = []
      data = op.data
      if data.chunk_shape[1] > 1:
          data = yield from recursive_tile(
              data.rechunk({1: op.data.shape[1]})
          )
  
      for in_chunk in data.chunks:
          chunk_op = op.copy().reset_key()
          chunk_index = (in_chunk.index[0],)
  
          if len(out.shape) > 1:
              chunk_shape = (in_chunk.shape[0], out.shape[1])
              chunk_index += (0,)
          else:
              chunk_shape = (in_chunk.shape[0],)
  
          if op.output_types[0] == OutputType.tensor:
              out_chunk = chunk_op.new_chunk(
                  [in_chunk],
                  shape=chunk_shape,
                  dtype=out.dtype,
                  order=out.order,
                  index=chunk_index,
              )
          elif op.output_types[0] == OutputType.dataframe:
              # dataframe chunk
              out_chunk = chunk_op.new_chunk(
                  [in_chunk],
                  shape=chunk_shape,
                  dtypes=out.dtypes,
                  columns_value=out.columns_value,
                  index_value=in_chunk.index_value,
                  index=chunk_index,
              )
          else:
              # series chunk
              out_chunk = chunk_op.new_chunk(
                  [in_chunk],
                  shape=chunk_shape,
                  dtype=out.dtype,
                  index_value=in_chunk.index_value,
                  name=out.name,
                  index=chunk_index,
              )
          out_chunks.append(out_chunk)
  
      new_op = op.copy()
      params = out.params
      params["chunks"] = out_chunks
      nsplits = (data.nsplits[0],)
      if out.ndim > 1:
          nsplits += ((out.shape[1],),)
      params["nsplits"] = nsplits
      return new_op.new_tileables(op.inputs, kws=[params])
  
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 8 for calling the LGBMPredict obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op