code: def test_pandas_minmax_scaling():
      s1 = pd.Series([1, 2, 3, 4, 5, 6], index=(range(6)))
      s2 = pd.Series([10, 9, 8, 7, 6, 5], index=(range(6)))
      df = pd.DataFrame(s1, columns=["s1"])
      df["s2"] = s2
  
      df_out1 = minmax_scaling(df, ["s1", "s2"], min_val=0, max_val=1)
      df_out2 = minmax_scaling(
          df, ["s1", "s2"], min_val=50, max_val=100
      )
  
      ary_out1 = np.array(
          [
              [0.0, 1.0],
              [0.2, 0.8],
              [0.4, 0.6],
              [0.6, 0.4],
              [0.8, 0.2],
              [1.0, 0.0],
          ]
      )
  
      ary_out2 = np.array(
          [
              [50.0, 100.0],
              [60.0, 90.0],
              [70.0, 80.0],
              [80.0, 70.0],
              [90.0, 60.0],
              [100.0, 50.0],
          ]
      )
  
      np.testing.assert_allclose(df_out1.values, ary_out1, rtol=1e-03)
      assert (df_out2.values == ary_out2).all()
  
imports: import numpy
  import pandas
  from mlxtend.preprocessing import minmax_scaling
label_desc: Use image node_id 2 for calling a global function with example usage: test_pandas_minmax_scaling() without return types