docs: def inference(images):
    """Build the CIFAR-10 model.
  
    Args:
      images: Images returned from distorted_inputs() or inputs().
  
    Returns:
      Logits.
    """
    # We instantiate all variables using tf.get_variable() instead of
    # tf.Variable() in order to share variables across multiple GPU training runs.
    # If we only ran this model on a single GPU, we could simplify this function
    # by replacing all instances of tf.get_variable() with tf.Variable().
    #
    # conv1
    with tf.variable_scope('conv1') as scope:
      kernel = _variable_with_weight_decay('weights',
                                           shape=[5, 5, 3, 64],
                                           stddev=5e-2,
                                           wd=0.0)
      conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
      biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
      pre_activation = tf.nn.bias_add(conv, biases)
      conv1 = tf.nn.relu(pre_activation, name=scope.name)
      _activation_summary(conv1)
  
    # pool1
    pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                           padding='SAME', name='pool1')
    # norm1
    norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,
                      name='norm1')
  
    # conv2
    with tf.variable_scope('conv2') as scope:
      kernel = _variable_with_weight_decay('weights',
                                           shape=[5, 5, 64, 64],
                                           stddev=5e-2,
                                           wd=0.0)
      conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
      biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))
      pre_activation = tf.nn.bias_add(conv, biases)
      conv2 = tf.nn.relu(pre_activation, name=scope.name)
      _activation_summary(conv2)
  
    # norm2
    norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,
                      name='norm2')
    # pool2
    pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1],
                           strides=[1, 2, 2, 1], padding='SAME', name='pool2')
  
    # local3
    with tf.variable_scope('local3') as scope:
      # Move everything into depth so we can perform a single matrix multiply.
      reshape = tf.reshape(pool2, [FLAGS.batch_size, -1])
      dim = reshape.get_shape()[1].value
      weights = _variable_with_weight_decay('weights', shape=[dim, 384],
                                            stddev=0.04, wd=0.004)
      biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))
      local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
      _activation_summary(local3)
  
    # local4
    with tf.variable_scope('local4') as scope:
      weights = _variable_with_weight_decay('weights', shape=[384, 192],
                                            stddev=0.04, wd=0.004)
      biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
      local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)
      _activation_summary(local4)
  
    # linear layer(WX + b),
    # We don't apply softmax here because
    # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits
    # and performs the softmax internally for efficiency.
    with tf.variable_scope('softmax_linear') as scope:
      weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES],
                                            stddev=1/192.0, wd=0.0)
      biases = _variable_on_cpu('biases', [NUM_CLASSES],
                                tf.constant_initializer(0.0))
      softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
      _activation_summary(softmax_linear)
  
    return softmax_linear