docstr: Predict class for X.
  
  The predicted class of an input sample is a vote by the trees in
  the forest, weighted by their probability estimates. That is,
  the predicted class is the one with highest mean probability
  estimate across the trees.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      The input samples. Internally, its dtype will be converted to
      ``dtype=np.float32``. If a sparse matrix is provided, it will be
      converted into a sparse ``csr_matrix``.
  
  Returns
  -------
  y : ndarray of shape (n_samples,) or (n_samples, n_outputs)
      The predicted classes.
code: def predict(self, X):
      """
      Predict class for X.
  
      The predicted class of an input sample is a vote by the trees in
      the forest, weighted by their probability estimates. That is,
      the predicted class is the one with highest mean probability
      estimate across the trees.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          The input samples. Internally, its dtype will be converted to
          ``dtype=np.float32``. If a sparse matrix is provided, it will be
          converted into a sparse ``csr_matrix``.
  
      Returns
      -------
      y : ndarray of shape (n_samples,) or (n_samples, n_outputs)
          The predicted classes.
      """
      _patching_status = PatchingConditionsChain(
          "sklearn.ensemble.RandomForestRegressor.predict"
      )
      _dal_ready = _patching_status.and_conditions(
          [
              (
                  hasattr(self, "daal_model_"),
                  "oneDAL model was not trained.",
              ),
              (
                  not sp.issparse(X),
                  "X is sparse. Sparse input is not supported.",
              ),
          ]
      )
      if hasattr(self, "n_outputs_"):
          _dal_ready = _patching_status.and_conditions(
              [
                  (
                      self.n_outputs_ == 1,
                      f"Number of outputs ({self.n_outputs_}) is not 1.",
                  )
              ]
          )
  
      _patching_status.write_log()
      if not _dal_ready:
          return super().predict(X)
  
      if sklearn_check_version("1.0"):
          self._check_feature_names(X, reset=False)
      X = check_array(
          X,
          accept_sparse=["csr", "csc", "coo"],
          dtype=[np.float64, np.float32],
      )
      return self._daal_predict_regressor(X)
  
imports: import logging
  import numbers
  import warnings
  from math import ceil
  import numpy
  from scipy import sparse
  from sklearn.base import clone
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.exceptions import DataConversionWarning
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.tree._tree import Tree
  from sklearn.utils import check_array, check_random_state, deprecated
  from sklearn.utils.validation import _num_samples, check_consistent_length, check_is_fitted
  import daal4py
  from daal4py.sklearn._utils import PatchingConditionsChain, check_tree_nodes, control_n_jobs, daal_check_version, getFPType, run_with_n_jobs, sklearn_check_version
  from .._device_offload import support_usm_ndarray
  from ..utils.validation import _daal_num_features
label_desc: Use image node_id 2 for calling the RandomForestRegressor obj's underlying member method code with example usage: obj.predict(X) and returns: self, super