code: def _create_model(self, module, policy):
      m = module.model()
  
      coefficients = self.coef_
      dtype = get_dtype(coefficients)
      coefficients = np.asarray(coefficients, dtype=dtype)
  
      if coefficients.ndim == 2:
          n_features_in = coefficients.shape[1]
          assert coefficients.shape[0] == 1
      else:
          n_features_in = coefficients.size
  
      intercept = self.intercept_
      if not isinstance(intercept, Number):
          intercept = np.asarray(intercept, dtype=dtype)
          assert intercept.size == 1
  
      intercept = _check_array(
          intercept,
          dtype=[np.float64, np.float32],
          force_all_finite=True,
          ensure_2d=False,
      )
      coefficients = _check_array(
          coefficients,
          dtype=[np.float64, np.float32],
          force_all_finite=True,
          ensure_2d=False,
      )
  
      coefficients, intercept = make2d(coefficients), make2d(intercept)
  
      assert coefficients.shape == (1, n_features_in)
      assert intercept.shape == (1, 1)
  
      desired_shape = (1, n_features_in + 1)
      packed_coefficients = np.zeros(desired_shape, dtype=dtype)
  
      packed_coefficients[:, 1:] = coefficients
      if self.fit_intercept:
          packed_coefficients[:, 0][:, np.newaxis] = intercept
  
      packed_coefficients = _convert_to_supported(
          policy, packed_coefficients
      )
  
      m.packed_coefficients = to_table(packed_coefficients)
  
      self._onedal_model = m
  
      return m
  
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 5 for calling the BaseLogisticRegression obj's underlying member method code with example usage: obj._create_model(module, policy) and returns: m