code: def setUpClass(cls):
      # define placeholders
      cls.x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
      cls.y_ = tf.placeholder(tf.int64, shape=[None], name="y_")
  
      # define the network
      network = tl.layers.InputLayer(cls.x, name="input")
      network = tl.layers.DropoutLayer(network, keep=0.8, name="drop1")
      network = tl.layers.DenseLayer(
          network, n_units=100, act=tf.nn.relu, name="relu1"
      )
      network = tl.layers.DropoutLayer(network, keep=0.8, name="drop2")
      network = tl.layers.DenseLayer(
          network, n_units=100, act=tf.nn.relu, name="relu2"
      )
      network = tl.layers.DropoutLayer(network, keep=0.8, name="drop3")
  
      # the softmax is implemented internally in tl.cost.cross_entropy(y, y_) to
      # speed up computation, so we use identity here.
      # see tf.nn.sparse_softmax_cross_entropy_with_logits()
      cls.network = tl.layers.DenseLayer(
          network, n_units=10, name="output"
      )
  
      # define cost function and metric.
      y = cls.network.outputs
      cls.cost = tl.cost.cross_entropy(y, cls.y_, name="cost")
  
      correct_prediction = tf.equal(tf.argmax(y, 1), cls.y_)
  
      cls.acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
      # y_op = tf.argmax(tf.nn.softmax(y), 1)
  
      # define the optimizer
      train_params = cls.network.trainable_weights
      cls.train_op = tf.train.AdamOptimizer(
          learning_rate=0.0001
      ).minimize(cls.cost, var_list=train_params)
  
file_path: TensorLayer/tests/pending/test_mnist_simple.py
imports: import os
  import unittest
  import tensorflow
  import tensorlayer
  from tests.utils import CustomTestCase
label_desc: Use image node_id 1 for calling the Simple_MNIST_Test obj's underlying member method code with example usage: obj.setUpClass(cls) without return types