code: def test_ridge(
      nrows,
      ncols,
      n_parts,
      fit_intercept,
      normalize,
      datatype,
      delayed,
      client,
  ):
      from cuml.dask.linear_model import Ridge as cumlRidge_dask
  
      n_info = 5
      nrows = int(nrows)
      ncols = int(ncols)
      X, y = make_regression_dataset(datatype, nrows, ncols, n_info)
  
      X_df, y_df = _prep_training_data(client, X, y, n_parts)
  
      lr = cumlRidge_dask(
          alpha=0.5, fit_intercept=fit_intercept, normalize=normalize
      )
  
      lr.fit(X_df, y_df)
  
      ret = lr.predict(X_df, delayed=delayed)
  
      error_cuml = mean_squared_error(
          y, ret.compute().to_pandas().values
      )
  
      assert error_cuml < 1e-1
  
file_path: cuml/python/cuml/tests/dask/test_dask_ridge_regression.py
imports: from cuml.internals.safe_imports import gpu_only_import
  import pytest
  from cuml.dask.common import utils
  from sklearn.metrics import mean_squared_error
  from sklearn.datasets import make_regression
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 3 for calling a global function with example usage: test_ridge(nrows, ncols, n_parts, fit_intercept, normalize, datatype, delayed, client) without return types