code: def test_handle_zeros_in_scale():
      s2 = handle_zeros_in_scale(s)
      a2 = handle_zeros_in_scale(a)
  
      assert list(s2.compute()) == [1, 1, 2, 3, 1]
      assert list(a2.compute()) == [1, 1, 2, 3, 1]
  
      x = np.array([1, 2, 3, 0], dtype="f8")
      expected = np.array([1, 2, 3, 1], dtype="f8")
      result = handle_zeros_in_scale(x)
      np.testing.assert_array_equal(result, expected)
  
      x = pd.Series(x)
      expected = pd.Series(expected)
      result = handle_zeros_in_scale(x)
      tm.assert_series_equal(result, expected)
  
      x = da.from_array(x.values, chunks=2)
      expected = expected.values
      result = handle_zeros_in_scale(x)
      assert_eq_ar(result, expected)
  
      x = dd.from_dask_array(x)
      expected = pd.Series(expected)
      result = handle_zeros_in_scale(x)
      assert_eq_df(result, expected)
  
imports: from collections import namedtuple
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pandas.testing
  import pytest
  from dask.array.utils import assert_eq
  from dask.dataframe.utils import assert_eq
  from dask_ml.datasets import make_classification
  from dask_ml.utils import _num_samples, assert_estimator_equal, check_array, check_chunks, check_matching_blocks, check_random_state, handle_zeros_in_scale, slice_columns
label_desc: Use image node_id 2 for calling a global function with example usage: test_handle_zeros_in_scale() without return types