code: def test_eval():
      input_dim = 2
      proj_dim = 2
  
      x = C.input_variable(shape=(input_dim,))
      W = C.parameter(
          shape=(input_dim, proj_dim), init=[[1, 0], [0, 1]]
      )
      B = C.parameter(shape=(proj_dim,), init=[[0, 1]])
      t = C.times(x, W)
      z = t + B
  
      labels = C.input_variable(shape=(proj_dim,))
      pe = classification_error(z, labels)
  
      tester = C.eval.Evaluator(pe)
  
      x_value = [[0, 1], [2, 2]]
      label_value = [[0, 1], [1, 0]]
      arguments = {x: x_value, labels: label_value}
      eval_error = tester.test_minibatch(arguments)
  
      assert np.allclose(eval_error, 0.5)
  
imports: import numpy
  from cntk.metrics import classification_error
  import cntk
label_desc: Use image node_id 1 for calling a global function with example usage: test_eval() without return types