import tensorflow as tf

    return images, sparse_labels

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

def conv_scale(x, W):
  return tf.nn.conv3d(x, W, strides=[1,1,1,1,1], padding='VALID')


def inference(x):
  """ Creates a model with pooling across space and scales.
