code: def tile(cls, op: "BlockwiseEnsemblePredict"):
      out = op.outputs[0]
      x = op.x
      estimators = op.estimators[0]
      estimators_chunks = estimators.chunks
  
      out_chunks = []
      for chunk in x.chunks:
          chunk_op = op.copy().reset_key()
          if out.ndim == 2:
              chunk_shape = (chunk.shape[0], out.shape[1])
              chunk_index = (chunk.index[0], 0)
          else:
              chunk_shape = (chunk.shape[0],)
              chunk_index = (chunk.index[0],)
          out_chunk = chunk_op.new_chunk(
              [chunk] + estimators_chunks,
              shape=chunk_shape,
              dtype=out.dtype,
              order=out.order,
              index=chunk_index,
          )
          out_chunks.append(out_chunk)
  
      if out.ndim == 2:
          nsplits = (x.nsplits[0], (out.shape[1],))
      else:
          nsplits = (x.nsplits[0],)
      params = out.params.copy()
      params["nsplits"] = nsplits
      params["chunks"] = out_chunks
      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 BlockwiseEnsemblePredict obj's underlying member method code with example usage: obj.tile(cls, op) and returns: op