class_docstr: Abstract base class for Gradient Boosting.
class_docstr_tok: ["Abstract","base","class","for","Gradient","Boosting","."]
docstr: Check input and compute raw predictions of the init estimator.
code: def _raw_predict_init(self, X):
      """Check input and compute raw predictions of the init estimator."""
      self._check_initialized()
      X = self.estimators_[0, 0]._validate_X_predict(
          X, check_input=True
      )
      if self.init_ == "zero":
          raw_predictions = np.zeros(
              shape=(X.shape[0], self._loss.K), dtype=np.float64
          )
      else:
          raw_predictions = self._loss.get_init_raw_predictions(
              X, self.init_
          ).astype(np.float64)
      return raw_predictions
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  from time import time
  import numpy
  from scipy.sparse import csc_matrix, csr_matrix, issparse
  from ..base import ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ..exceptions import NotFittedError
  from ..model_selection import train_test_split
  from ..tree import DecisionTreeRegressor
  from ..tree._tree import DOUBLE, DTYPE
  from ..utils import check_array, check_random_state, column_or_1d
  from ..utils._param_validation import HasMethods, Interval, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _check_sample_weight, check_is_fitted
  from .None import _gb_losses
  from ._base import BaseEnsemble
  from ._gradient_boosting import _random_sample_mask, predict_stage, predict_stages
label_desc: Use image node_id 13 for calling the BaseGradientBoosting obj's underlying member method code with example usage: obj._raw_predict_init(X) and returns: raw_predictions