code: def test_sparse_svr_pickle(tmpdir, datatype, nrows, ncols, n_info):
      """
      A separate test to cover the case when the SVM model
      parameters are sparse. Spares input alone does not
      guarantee that the model parameters (SvmModel.support_matrix)
      are sparse (a dense representation can be chosen for
      performance reason). The large number of features used
      here will result in a sparse model representation.
      """
      result = {}
  
      def create_mod():
          X_train = scipy_sparse.random(
              nrows,
              ncols,
              density=0.001,
              format="csr",
              dtype=datatype,
              random_state=42,
          )
          y_train = np.random.RandomState(42).rand(nrows)
          X_test = X_train
          model = cuml.svm.SVR(max_iter=1)
          model.fit(X_train, y_train)
          result["svr"] = model.predict(X_test)
          return model, X_test
  
      def assert_model(pickled_model, X_test):
          assert array_equal(
              result["svr"], pickled_model.predict(X_test)
          )
  
      pickle_save_load(tmpdir, create_mod, assert_model)
  
imports: from sklearn.model_selection import train_test_split
  from sklearn.manifold import trustworthiness
  from sklearn.datasets import load_iris, make_classification, make_regression
  from sklearn.base import clone
  from cuml.testing.utils import array_equal, unit_param, stress_param, ClassEnumerator, get_classes_from_package, compare_svm, compare_probabilistic_svm
  from cuml.tsa.arima import ARIMA
  import pytest
  import pickle
  import cuml
  from cuml.internals.safe_imports import cpu_only_import, cpu_only_import_from
label_desc: Use image node_id 24 for calling a global function with example usage: test_sparse_svr_pickle(tmpdir, datatype, nrows, ncols, n_info) and returns: model,  X_test