code: def predict(self, X):
      return X[:, self.component] * self.coef_
  
imports: import numbers
  import numpy
  from scipy.sparse import csc_matrix, csr_matrix, issparse
  from sklearn.base import BaseEstimator
  from sklearn.ensemble._base import BaseEnsemble
  from sklearn.ensemble._gb import BaseGradientBoosting, VerboseReporter
  from sklearn.ensemble._gradient_boosting import _random_sample_mask
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.tree._tree import DTYPE
  from sklearn.utils import check_consistent_length, check_random_state, column_or_1d
  from sklearn.utils._param_validation import Interval, StrOptions
  from sklearn.utils.extmath import squared_norm
  from sklearn.utils.validation import check_is_fitted
  from ..base import SurvivalAnalysisMixin
  from ..linear_model.coxph import BreslowEstimator
  from ..util import check_array_survival
  from .survival_loss import LOSS_FUNCTIONS, CensoredSquaredLoss, CoxPH, IPCWLeastSquaresError
label_desc: Use image node_id 3 for calling the ComponentwiseLeastSquares obj's underlying member method code with example usage: obj.predict(X) and returns: unknown