code: def _infer(self, X, module, queue):
      _check_is_fitted(self)
  
      X = _check_array(
          X,
          dtype=[np.float64, np.float32],
          force_all_finite=True,
          ensure_2d=False,
      )
      _check_n_features(self, X, False)
  
      X = make2d(X)
      policy = self._get_policy(queue, X)
  
      if hasattr(self, "_onedal_model"):
          model = self._onedal_model
      else:
          model = self._create_model(module, policy)
  
      X = _convert_to_supported(policy, X)
      params = self._get_onedal_params(get_dtype(X))
  
      X_table = to_table(X)
      result = module.infer(policy, params, model, X_table)
      return result
  
imports: from abc import ABCMeta, abstractmethod
  from numbers import Number
  import numpy
  from sklearn.base import BaseEstimator
  from daal4py.sklearn._utils import get_dtype, make2d
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted
  from ..common._mixin import ClassifierMixin
  from ..common._policy import _get_policy
  from ..datatypes import _convert_to_supported, from_table, to_table
  from ..utils import _check_array, _check_n_features, _check_X_y, _num_features, _type_of_target
label_desc: Use image node_id 6 for calling the BaseLogisticRegression obj's underlying member method code with example usage: obj._infer(X, module, queue) and returns: result