code: def test_single_input_regression(client, ignore_empty_partitions):
      X, y = make_classification(n_samples=1, n_classes=1)
      X = X.astype(np.float32)
      y = y.astype(np.float32)
  
      X, y = _prep_training_data(client, X, y, partitions_per_worker=2)
      cu_rf_mg = cuRFR_mg(
          n_bins=1, ignore_empty_partitions=ignore_empty_partitions
      )
  
      if (
          ignore_empty_partitions
          or len(client.scheduler_info()["workers"].keys()) == 1
      ):
          cu_rf_mg.fit(X, y)
          cuml_mod_predict = cu_rf_mg.predict(X)
          cuml_mod_predict = cp.asnumpy(
              cp.array(cuml_mod_predict.compute())
          )
          y = cp.asnumpy(cp.array(y.compute()))
          assert y[0] == cuml_mod_predict[0]
  
      else:
          with pytest.raises(ValueError):
              cu_rf_mg.fit(X, y)
  
imports: from dask.distributed import Client
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import accuracy_score, r2_score, mean_squared_error
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import make_regression, make_classification
  from dask.array import from_array
  from cuml.ensemble import RandomForestRegressor
  from cuml.ensemble import RandomForestClassifier
  from cuml.dask.common import utils
  from cuml.dask.ensemble import RandomForestRegressor
  from cuml.dask.ensemble import RandomForestClassifier
  from cuml.internals.safe_imports import cpu_only_import
  import json
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 8 for calling a global function with example usage: test_single_input_regression(client, ignore_empty_partitions) without return types