docstr: Predict using the linear model
  
  Parameters
  ----------
  X : array-like or sparse matrix, shape = (n_samples, n_features)
      Samples.
  
  Returns
  -------
  C : array, shape = (n_samples,)
      Returns predicted values.
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code: def predict(self, X):
      """Predict using the linear model
  
      Parameters
      ----------
      X : array-like or sparse matrix, shape = (n_samples, n_features)
          Samples.
  
      Returns
      -------
      C : array, shape = (n_samples,)
          Returns predicted values.
      """
  
      if sklearn_check_version("1.0"):
          self._check_feature_names(X, reset=False)
  
      X = check_array(
          X,
          accept_sparse=["csr", "csc", "coo"],
          dtype=[np.float64, np.float32],
      )
      good_shape_for_daal = (
          True
          if X.ndim <= 1
          else True
          if X.shape[0] >= X.shape[1]
          else False
      )
  
      _patching_status = PatchingConditionsChain(
          "sklearn.linear_model.ElasticNet.predict"
      )
      _dal_ready = _patching_status.and_conditions(
          [
              (
                  hasattr(self, "daal_model_"),
                  "oneDAL model was not trained.",
              ),
              (
                  not sp.issparse(X),
                  "X is sparse. Sparse input is not supported.",
              ),
              (
                  good_shape_for_daal,
                  "The shape of X does not satisfy oneDAL requirements: "
                  "number of features > number of samples.",
              ),
          ]
      )
      _patching_status.write_log()
  
      if not _dal_ready:
          return self._decision_function(X)
      return _daal4py_predict_enet(self, X)
  
imports: import numbers
  import numpy
  from scipy import sparse
  from sklearn.linear_model._coordinate_descent import ElasticNet
  from sklearn.linear_model._coordinate_descent import Lasso
  from sklearn.utils import check_array, check_X_y
  import daal4py
  from daal4py.sklearn._utils import PatchingConditionsChain, control_n_jobs, get_patch_message, getFPType, make2d, run_with_n_jobs, sklearn_check_version
  import logging
  import warnings
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.preprocessing import normalize
  from .._device_offload import support_usm_ndarray
label_desc: Use image node_id 1 for calling the ElasticNet obj's underlying member method code with example usage: obj.predict(X) and returns: _daal4py_predict_enet, self