code: def test_numpy_minmax_scaling():
      ary = np.array([[1, 10], [2, 9], [3, 8], [4, 7], [5, 6], [6, 5]])
  
      df_out1 = minmax_scaling(ary, [0, 1], min_val=0, max_val=1)
      df_out2 = minmax_scaling(ary, [0, 1], 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, ary_out1, rtol=1e-03)
      assert (df_out2 == ary_out2).all()
  
imports: import numpy
  import pandas
  from mlxtend.preprocessing import minmax_scaling
label_desc: Use image node_id 3 for calling a global function with example usage: test_numpy_minmax_scaling() without return types