code: def test_normalizer():
      """Tests basic Normalizer functionality."""
  
      col_a = [10, 10, 10]
      col_b = [1, 3, 3]
      col_c = [2, 4, -4]
      in_df = pd.DataFrame.from_dict(
          {"A": col_a, "B": col_b, "C": col_c}
      )
      ds = ray.data.from_pandas(in_df)
  
      # l2 norm
      normalizer = Normalizer(["B", "C"])
      transformed = normalizer.transform(ds)
      out_df = transformed.to_pandas()
  
      processed_col_a = col_a
      processed_col_b = [1 / np.sqrt(5), 0.6, 0.6]
      processed_col_c = [2 / np.sqrt(5), 0.8, -0.8]
      expected_df = pd.DataFrame.from_dict(
          {
              "A": processed_col_a,
              "B": processed_col_b,
              "C": processed_col_c,
          }
      )
  
      assert out_df.equals(expected_df)
  
      # l1 norm
      normalizer = Normalizer(["B", "C"], norm="l1")
  
      transformed = normalizer.transform(ds)
      out_df = transformed.to_pandas()
  
      processed_col_a = col_a
      processed_col_b = [1 / 3, 3 / 7, 3 / 7]
      processed_col_c = [2 / 3, 4 / 7, -4 / 7]
      expected_df = pd.DataFrame.from_dict(
          {
              "A": processed_col_a,
              "B": processed_col_b,
              "C": processed_col_c,
          }
      )
  
      assert out_df.equals(expected_df)
  
      # max norm
      normalizer = Normalizer(["B", "C"], norm="max")
  
      transformed = normalizer.transform(ds)
      out_df = transformed.to_pandas()
  
      processed_col_a = col_a
      processed_col_b = [0.5, 0.75, 0.75]
      processed_col_c = [1.0, 1.0, -1.0]
      expected_df = pd.DataFrame.from_dict(
          {
              "A": processed_col_a,
              "B": processed_col_b,
              "C": processed_col_c,
          }
      )
  
      assert out_df.equals(expected_df)
  
imports: import numpy
  import pandas
  import pytest
  import ray
  from ray.data.preprocessors import Normalizer
label_desc: Use image node_id 1 for calling a global function with example usage: test_normalizer() without return types