code: def test_lbfgs(
      nrows,
      ncols,
      n_parts,
      fit_intercept,
      datatype,
      delayed,
      client,
      penalty="l2",
      l1_ratio=None,
      C=1.0,
      n_classes=2,
      convert_to_sparse=False,
  ):
      tolerance = 0.005
  
      def imp():
          import cuml.comm.serialize  # NOQA
  
      client.run(imp)
  
      from cuml.dask.linear_model.logistic_regression import (
          LogisticRegression as cumlLBFGS_dask,
      )
  
      # set n_informative variable for calling sklearn.datasets.make_classification
      n_info = 5
      nrows = int(nrows)
      ncols = int(ncols)
      X, y = make_classification_dataset(
          datatype, nrows, ncols, n_info, n_classes=n_classes
      )
  
      if convert_to_sparse:
          # X_dask and y_dask are dask array
          X_dask, y_dask = _prep_training_data_sparse(
              client, X, y, n_parts
          )
      else:
          # X_dask and y_dask are dask cudf
          X_dask, y_dask = _prep_training_data(client, X, y, n_parts)
  
      lr = cumlLBFGS_dask(
          solver="qn",
          fit_intercept=fit_intercept,
          penalty=penalty,
          l1_ratio=l1_ratio,
          C=C,
          verbose=True,
      )
      lr.fit(X_dask, y_dask)
  
      def array_to_numpy(ary):
          if isinstance(ary, cp.ndarray):
              return cp.asarray(ary)
          elif isinstance(ary, cudf.DataFrame) or isinstance(
              ary, cudf.Series
          ):
              return ary.to_numpy()
          else:
              assert isinstance(ary, np.ndarray)
              return ary
  
      lr_coef = array_to_numpy(lr.coef_)
      lr_intercept = array_to_numpy(lr.intercept_)
  
      if penalty == "l2" or penalty == "none":
          sk_solver = "lbfgs"
      elif penalty == "l1" or penalty == "elasticnet":
          sk_solver = "saga"
      else:
          raise ValueError(f"unexpected penalty {penalty}")
  
      sk_model = skLR(
          solver=sk_solver,
          fit_intercept=fit_intercept,
          penalty=penalty,
          l1_ratio=l1_ratio,
          C=C,
      )
      sk_model.fit(X, y)
      sk_coef = sk_model.coef_
      sk_intercept = sk_model.intercept_
  
      if sk_solver == "lbfgs":
          assert len(lr_coef) == len(sk_coef)
          assert array_equal(
              lr_coef, sk_coef, tolerance, with_sign=True
          )
          assert array_equal(
              lr_intercept, sk_intercept, tolerance, with_sign=True
          )
  
      # test predict
      cu_preds = lr.predict(X_dask, delayed=delayed).compute()
      if isinstance(cu_preds, cp.ndarray):
          cu_preds = cp.asnumpy(cu_preds)
      if not isinstance(cu_preds, np.ndarray):
          cu_preds = cu_preds.to_numpy()
      accuracy_cuml = accuracy_score(y, cu_preds)
  
      sk_preds = sk_model.predict(X)
      accuracy_sk = accuracy_score(y, sk_preds)
  
      assert len(cu_preds) == len(sk_preds)
      assert (accuracy_cuml >= accuracy_sk) | (
          np.abs(accuracy_cuml - accuracy_sk) < 1e-3
      )
  
      return lr
  
imports: from cuml.internals.safe_imports import gpu_only_import
  import pytest
  from cuml.dask.common import utils
  from functools import partial
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.datasets import make_classification
  from sklearn.linear_model import LogisticRegression
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.testing.utils import array_equal
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 8 for calling a global function with example usage: test_lbfgs(nrows, ncols, n_parts, fit_intercept, datatype, delayed, client, penalty, l1_ratio, C, n_classes, convert_to_sparse) and returns: lr, cp, ary, ary