code: def test_dataframes():
      df = pd.DataFrame({"x": range(10), "y": [0, 1] * 5})
      ddf = dd.from_pandas(df, npartitions=2)
  
      with dask.config.set(scheduler="single-threaded"):
          sgd = SGDClassifier(max_iter=5, tol=1e-3)
  
          sgd = fit(sgd, ddf[["x"]], ddf.y, classes=[0, 1])
  
          sol = sgd.predict(df[["x"]])
          result = predict(sgd, ddf[["x"]])
  
          da.utils.assert_eq(sol, result)
  
imports: import dask
  import dask.array
  import dask.bag
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  from dask.delayed import Delayed
  from sklearn.base import clone
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import SGDClassifier
  import dask_ml.feature_extraction.text
  from dask_ml._partial import fit, predict
  from dask_ml.datasets import make_classification
  from dask_ml.wrappers import Incremental
label_desc: Use image node_id 5 for calling a global function with example usage: test_dataframes() without return types