class_docstr: Blockwise training and ensemble voting classifier.
  
  This classifier trains on blocks / partitions of tensors or DataFrames.
  A cloned version of `estimator` will be fit *independently* on each block
  or partition of the data. This is useful when the sub estimator
  only works on small in-memory data structures like a NumPy array or pandas
  DataFrame.
  
  Prediction is done by the *ensemble* of learned models.
  
  .. warning::
  
     Ensure that your data are sufficiently shuffled prior to training!
     If the values of the various blocks / partitions of your dataset are not
     distributed similarly, the classifier will give poor results.
  
  Parameters
  ----------
  estimator : Estimator
  voting : str, {'hard', 'soft'} (default='hard')
      If 'hard', uses predicted class labels for majority rule voting.
      Else if 'soft', predicts the class label based on the argmax of
      the sums of the predicted probabilities, which is recommended for
      an ensemble of well-calibrated classifiers.
  classes : list-like, optional
      The set of classes that `y` can take. This can also be provided as
      a fit param if the underlying estimator requires `classes` at fit time.
  
  Attributes
  ----------
  estimators_ : list of classifiers
      The collection of fitted sub-estimators that are `estimator` fitted
      on each partition / block of the inputs.
  
  classes_ : array-like, shape (n_predictions,)
      The class labels.
  
  Examples
  --------
  >>> import mars.tensor as mt
  >>> from mars.learn.ensemble import BlockwiseVotingClassifier
  >>> from sklearn.linear_model import RidgeClassifier
  >>> from sklearn.datasets import make_classification
  >>> X, y = make_classification(n_samples=100_000)
  >>> X, y = mt.tensor(X, chunk_size=10_0000), mt.tensor(y, chunk_size=10_0000)
  >>> subestimator = RidgeClassifier(random_state=0)
  >>> clf = BlockwiseVotingClassifier(subestimator)
  >>> clf.fit(X, y)
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code: def predict(
      self, X, session: SessionType = None, run_kwargs: dict = None
  ):
      check_is_fitted(self, attributes=["estimators_"])
      X = check_array(X)
      op = BlockwiseEnsemblePredict(
          x=X,
          estimators=[self.estimators_],
          voting=self.voting,
          proba=False,
          is_classifier=True,
          n_classes=self.n_classes_,
      )
      return op().execute(session=session, **(run_kwargs or dict()))
  
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 BlockwiseVotingClassifier obj's underlying member method code with example usage: obj.predict(X, session, run_kwargs) and returns: op