code: def _predict(self, X, module, queue):
      _check_is_fitted(self)
  
      policy = self._get_policy(queue, X)
  
      if isinstance(X, np.ndarray):
          X_loc = np.asarray(X)
      else:
          X_loc = X
  
      # Finiteness is checked in the sklearnex wrapper
      X_loc = _check_array(
          X_loc,
          dtype=[np.float64, np.float32],
          force_all_finite=False,
          ensure_2d=False,
      )
      _check_n_features(self, X_loc, False)
  
      if hasattr(self, "_onedal_model"):
          model = self._onedal_model
      else:
          model = self._create_model(module, policy)
  
      X_loc = make2d(X_loc)
      X_loc = _convert_to_supported(policy, X_loc)
      params = self._get_onedal_params(get_dtype(X_loc))
  
      X_table = to_table(X_loc)
      result = module.infer(policy, params, model, X_table)
      y = from_table(result.responses)
  
      if not isinstance(self.coef_, np.ndarray):
          coefficients = np.asarray(self.coef_)
      else:
          coefficients = self.coef_
  
      if y.shape[1] == 1 and coefficients.ndim == 1:
          return y.ravel()
      else:
          return y
  
imports: from abc import ABCMeta, abstractmethod
  from numbers import Number
  import numpy
  from sklearn.base import BaseEstimator
  from daal4py.sklearn._utils import daal_check_version, get_dtype, make2d
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted
  from ..common._mixin import RegressorMixin
  from ..common._policy import _get_policy
  from ..common.hyperparameters import get_hyperparameters
  from ..datatypes import _convert_to_supported, from_table, to_table
  from ..utils import _check_array, _check_n_features, _check_X_y, _num_features
label_desc: Use image node_id 6 for calling the BaseLinearRegression obj's underlying member method code with example usage: obj._predict(X, module, queue) and returns: y, y