code: def setUp(self):
      super(EvaluateOnceTest, self).setUp()
  
      # Create an easy training set:
      np.random.seed(0)
  
      self._inputs = np.zeros((16, 4))
      self._labels = np.random.randint(0, 2, size=(16, 1)).astype(
          np.float32
      )
  
      for i in range(16):
          j = int(2 * self._labels[i] + np.random.randint(0, 2))
          self._inputs[i, j] = 1
  
imports: import os
  import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.layers import layers
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import metrics
  from tensorflow.python.ops import state_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops.losses import losses
  from tensorflow.python.platform import test
  from tensorflow.python.training import basic_session_run_hooks
  from tensorflow.python.training import evaluation
  from tensorflow.python.training import gradient_descent
  from tensorflow.python.training import monitored_session
  from tensorflow.python.training import saver
  from tensorflow.python.training import training
label_desc: Use image node_id 1 for calling the EvaluateOnceTest obj's underlying member method code with example usage: obj.setUp() without return types