code: def execute(
      cls, ctx: Union[dict, Context], op: "BlockwiseEnsemblePredict"
  ):
      x = ctx[op.inputs[0].key]
      estimators = [ctx[inp.key] for inp in op.inputs[1:]]
      if op.proba or op.voting == "soft":
          predictions = [
              estimator.predict_proba(x) for estimator in estimators
          ]
      else:
          predictions = [
              estimator.predict(x) for estimator in estimators
          ]
  
      if op.is_classifier:
          if not op.proba:
              result = cls._execute_classifier_predict(predictions, op)
          else:
              result = cls._execute_classifier_predict_proba(
                  predictions, op
              )
      else:
          result = cls._execute_regressor_predict(predictions)
      ctx[op.outputs[0].key] = result
  
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 4 for calling the BlockwiseEnsemblePredict obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types