code: def tile(cls, op: "BlockwiseEnsembleFit"):
      X, y = op.x, op.y
      x_split = X.nsplits[0]
      y_split = y.nsplits[0]
      out = op.outputs[0]
  
      if any(np.isnan(s) for s in x_split + y_split) or np.isnan(
          X.shape[1]
      ):  # pragma: no cover
          yield
  
      if x_split != y_split or X.chunk_shape[1] > 1:
          x_split = y_split = decide_unify_split(x_split, y_split)
          X = X.rechunk({0: x_split, 1: X.shape[1]})
          y = y.rechunk({0: y_split})
          X, y = yield from recursive_tile(X, y)
  
      out_chunks = []
      for i, _ in enumerate(x_split):
          chunk_op = op.copy().reset_key()
          out_chunk = chunk_op.new_chunk(
              [X.cix[i, 0], y.cix[(i,)]],
              index=(i,),
          )
          out_chunks.append(out_chunk)
  
      params = out.params.copy()
      params["chunks"] = out_chunks
      params["nsplits"] = ((np.nan,) * len(x_split),)
      return op.copy().new_tileables(op.inputs, kws=[params])
  
imports: from typing import List, Union
  import numpy
  from sklearn.base import BaseEstimator, clone
  from sklearn.utils.validation import check_is_fitted
  from ...None import opcodes
  from ...None import execute
  from ...None import tensor
  from ...core import OutputType, ENTITY_TYPE, recursive_tile
  from ...core.context import Context
  from ...serialization.serializables import FieldTypes, AnyField, BoolField, DictField, Int64Field, ListField, StringField, KeyField
  from ...typing import SessionType
  from ...tensor.core import Tensor, TensorOrder
  from ...tensor.utils import decide_unify_split
  from ..operands import LearnOperand, LearnOperandMixin
  from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
  from ..utils import check_array
label_desc: Use image node_id 3 for calling the BlockwiseEnsembleFit obj's underlying member method code with example usage: obj.tile(cls, op) and returns: op