code: def _deprecate_normalize(normalize, estimator_name):
      """Normalize is to be deprecated from linear models and a use of
      a pipeline with a StandardScaler is to be recommended instead.
      Here the appropriate message is selected to be displayed to the user
      depending on the default normalize value (as it varies between the linear
      models and normalize value selected by the user).
  
      Parameters
      ----------
      normalize : bool,
          normalize value passed by the user
  
      estimator_name : str
          name of the linear estimator which calls this function.
          The name will be used for writing the deprecation warnings
  
      Returns
      -------
      normalize : bool,
          normalize value which should further be used by the estimator at this
          stage of the depreciation process
  
      Notes
      -----
      This function should be completely removed in 1.4.
      """
  
      if normalize not in [True, False, "deprecated"]:
          raise ValueError(
              "Leave 'normalize' to its default value or set it to True or False"
          )
  
      if normalize == "deprecated":
          _normalize = False
      else:
          _normalize = normalize
  
      pipeline_msg = (
          "If you wish to scale the data, use Pipeline with a StandardScaler "
          "in a preprocessing stage. To reproduce the previous behavior:\n\n"
          "from sklearn.pipeline import make_pipeline\n\n"
          "model = make_pipeline(StandardScaler(with_mean=False), "
          f"{estimator_name}())\n\n"
          "If you wish to pass a sample_weight parameter, you need to pass it "
          "as a fit parameter to each step of the pipeline as follows:\n\n"
          "kwargs = {s[0] + '__sample_weight': sample_weight for s "
          "in model.steps}\n"
          "model.fit(X, y, **kwargs)\n\n"
      )
  
      alpha_msg = ""
      if "LassoLars" in estimator_name:
          alpha_msg = "Set parameter alpha to: original_alpha * np.sqrt(n_samples). "
  
      if normalize != "deprecated" and normalize:
          warnings.warn(
              "'normalize' was deprecated in version 1.2 and will be removed in 1.4.\n"
              + pipeline_msg
              + alpha_msg,
              FutureWarning,
          )
      elif not normalize:
          warnings.warn(
              (
                  "'normalize' was deprecated in version 1.2 and will be "
                  "removed in 1.4. "
                  "Please leave the normalize parameter to its default value to "
                  "silence this warning. The default behavior of this estimator "
                  "is to not do any normalization. If normalization is needed "
                  "please use sklearn.preprocessing.StandardScaler instead."
              ),
              FutureWarning,
          )
  
      return _normalize
  
imports: import numbers
  import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral
  import numpy
  import scipy.sparse
  from scipy import linalg, optimize, sparse
  from scipy.sparse.linalg import lsqr
  from scipy.special import expit
  from ..base import BaseEstimator, ClassifierMixin, MultiOutputMixin, RegressorMixin, _fit_context
  from ..preprocessing._data import _is_constant_feature
  from ..utils import check_array, check_random_state
  from ..utils._array_api import get_namespace
  from ..utils._seq_dataset import ArrayDataset32, ArrayDataset64, CSRDataset32, CSRDataset64
  from ..utils.extmath import _incremental_mean_and_var, safe_sparse_dot
  from ..utils.parallel import Parallel, delayed
  from ..utils.sparsefuncs import inplace_column_scale, mean_variance_axis
  from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted
label_desc: Use image node_id 1 for calling a global function with example usage: _deprecate_normalize(normalize, estimator_name) and returns: _normalize