parameters: (eval_data)
return_statement: return images, labels
docstring: Construct input for CIFAR evaluation using the Reader ops.
  
    Args:
      eval_data: bool, indicating if one should use the train or eval data set.
  
    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
  
    Raises:
      ValueError: If no data_dir
docstring_summary: Construct input for CIFAR evaluation using the Reader ops.
docstring_tokens: Construct input for CIFAR evaluation using the Reader ops .
function: def inputs(eval_data):
    """Construct input for CIFAR evaluation using the Reader ops.
  
    Args:
      eval_data: bool, indicating if one should use the train or eval data set.
  
    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
  
    Raises:
      ValueError: If no data_dir
    """
    if not FLAGS.data_dir:
      raise ValueError('Please supply a data_dir')
    data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
    images, labels = cifar10_input.inputs(eval_data=eval_data,
                                          data_dir=data_dir,
                                          batch_size=FLAGS.batch_size)
    if FLAGS.use_fp16:
      images = tf.cast(images, tf.float16)
      labels = tf.cast(labels, tf.float16)
    return images, labels
function_tokens: def inputs ( eval_data ) : if not FLAGS . data_dir : raise ValueError ( 'Please supply a data_dir' ) data_dir = os . path . join ( FLAGS . data_dir , 'cifar-10-batches-bin' ) images , labels = cifar10_input . inputs ( eval_data = eval_data , data_dir = data_dir , batch_size = FLAGS . batch_size ) if FLAGS . use_fp16 : images = tf . cast ( images , tf . float16 ) labels = tf . cast ( labels , tf . float16 ) return images , labels