class_docstr: Base class for histogram-based gradient boosting estimators.
class_docstr_tok: ["Base","class","for","histogram-based","gradient","boosting","estimators","."]
docstr: Return the sum of the leaves values over all predictors.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      The input samples.
  n_threads : int, default=None
      Number of OpenMP threads to use. `_openmp_effective_n_threads` is called
      to determine the effective number of threads use, which takes cgroups CPU
      quotes into account. See the docstring of `_openmp_effective_n_threads`
      for details.
  
  Returns
  -------
  raw_predictions : array, shape (n_samples, n_trees_per_iteration)
      The raw predicted values.
docstr_tok: ["Return","the","sum","of","the","leaves","values","over","all","predictors",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_samples",",","n_features",")","The","input","samples",".","n_threads",":","int",",","default=None","Number","of","OpenMP","threads","to","use",".","`","_openmp_effective_n_threads","`","is","called","to","determine","the","effective","number","of","threads","use",",","which","takes","cgroups","CPU","quotes","into","account",".","See","the","docstring","of","`","_openmp_effective_n_threads","`","for","details",".","Returns","--","--","--","-","raw_predictions",":","array",",","shape","(","n_samples",",","n_trees_per_iteration",")","The","raw","predicted","values","."]
code: def _raw_predict(self, X, n_threads=None):
      """Return the sum of the leaves values over all predictors.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          The input samples.
      n_threads : int, default=None
          Number of OpenMP threads to use. `_openmp_effective_n_threads` is called
          to determine the effective number of threads use, which takes cgroups CPU
          quotes into account. See the docstring of `_openmp_effective_n_threads`
          for details.
  
      Returns
      -------
      raw_predictions : array, shape (n_samples, n_trees_per_iteration)
          The raw predicted values.
      """
      is_binned = getattr(self, "_in_fit", False)
      if not is_binned:
          X = self._validate_data(
              X, dtype=X_DTYPE, force_all_finite=False, reset=False
          )
      check_is_fitted(self)
      if X.shape[1] != self._n_features:
          raise ValueError(
              "X has {} features but this estimator was trained with "
              "{} features.".format(X.shape[1], self._n_features)
          )
      n_samples = X.shape[0]
      raw_predictions = np.zeros(
          shape=(n_samples, self.n_trees_per_iteration_),
          dtype=self._baseline_prediction.dtype,
          order="F",
      )
      raw_predictions += self._baseline_prediction
  
      # We intentionally decouple the number of threads used at prediction
      # time from the number of threads used at fit time because the model
      # can be deployed on a different machine for prediction purposes.
      n_threads = _openmp_effective_n_threads(n_threads)
      self._predict_iterations(
          X, self._predictors, raw_predictions, is_binned, n_threads
      )
      return raw_predictions
  
imports: import itertools
  from abc import ABC, abstractmethod
  from functools import partial
  from numbers import Integral, Real
  from timeit import default_timer
  import numpy
  from ..._loss.loss import _LOSSES, BaseLoss, HalfBinomialLoss, HalfGammaLoss, HalfMultinomialLoss, HalfPoissonLoss, PinballLoss
  from ...base import BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ...metrics import check_scoring
  from ...model_selection import train_test_split
  from ...preprocessing import LabelEncoder
  from ...utils import check_random_state, compute_sample_weight, resample
  from ...utils._openmp_helpers import _openmp_effective_n_threads
  from ...utils._param_validation import Interval, RealNotInt, StrOptions
  from ...utils.multiclass import check_classification_targets
  from ...utils.validation import _check_monotonic_cst, _check_sample_weight, check_consistent_length, check_is_fitted
  from ._gradient_boosting import _update_raw_predictions
  from .binning import _BinMapper
  from .common import G_H_DTYPE, X_DTYPE, Y_DTYPE
  from .grower import TreeGrower
label_desc: Use image node_id 15 for calling the BaseHistGradientBoosting obj's underlying member method code with example usage: obj._raw_predict(X, n_threads) and returns: raw_predictions