code: def test_bam_energy_function(self):
      input_vector = np.array([[1, 0, 0, 1, 1, 0, 0]])
      output_vector = np.array([[1, 0]])
      dbnet = algorithms.DiscreteBAM()
      dbnet.train(input_vector, output_vector)
  
      self.assertEqual(-7, dbnet.energy(input_vector, output_vector))
      self.assertEqual(
          0,
          dbnet.energy(
              np.array([[0, 0, 0, 0, 0, 0, 0]]), np.array([[0, 0]])
          ),
      )
      self.assertEqual(
          -7,
          dbnet.energy(
              np.array([[0, 1, 1, 0, 0, 1, 1]]), np.array([[0, 1]])
          ),
      )
  
      # Test 1d array
      self.assertEqual(
          -7,
          dbnet.energy(
              np.array([0, 1, 1, 0, 0, 1, 1]), np.array([0, 1])
          ),
      )
  
      # Test multiple input values energy calculation
      np.testing.assert_array_almost_equal(
          np.array([-7, 0]),
          dbnet.energy(
              np.array(
                  [
                      [0, 1, 1, 0, 0, 1, 1],
                      [0, 0, 0, 0, 0, 0, 0],
                  ]
              ),
              np.array(
                  [
                      [0, 1],
                      [0, 0],
                  ]
              ),
          ),
      )
  
file_path: neupy/tests/algorithms/memory/test_bam.py
imports: import pickle
  import numpy
  from neupy import algorithms
  from neupy.exceptions import NotTrained
  from algorithms.memory.data import zero, one, half_one, half_zero
  from base import BaseTestCase
  from helpers import vectors_for_testing
label_desc: Use image node_id 8 for calling the BAMTestCase obj's underlying member method code with example usage: obj.test_bam_energy_function() without return types