Case 1:

  input是1-level LoDTensor:
    input.lod = [[0, 2, 5, 7]]
    input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
    input.shape = [7, 1]

  输出为LoDTensor:
    out.shape = [3, 1]
    且 out.shape[0] == len(x.lod[-1]) == 3
    out.data = [[1.], [2.], [5.]], where 1.=first(1., 3.), 2.=first(2., 4., 6.), 5.=first(5., 1.)

Case 2:

  input是2-level的LoDTensor, 包含3个长度分别为[2, 0, 3]的序列，其中中间的0表示序列为空。
  第一个长度为2的序列包含2个长度分别为[1, 2]的子序列；
  最后一个长度为3的序列包含3个长度分别为[1, 0, 3]的子序列。
      input.lod = [[0, 2, 2, 5], [0, 1, 3, 4, 4, 7]]
      input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
      input.shape = [7, 1]

  将根据最后一层的lod信息[0, 1, 3, 4, 4, 7]进行池化操作，且pad_value = 0.0
  输出为LoDTensor：
      out.shape= [5, 1]
      out.lod = [[0, 2, 2, 5]]
      其中 out.shape[0] == len(x.lod[-1]) == 5
      out.data = [[1.], [3.], [4.], [0.0], [6.]]
      where 1.=first(1.), 3.=first(3., 2.), 4.=first(4.), 0.0 = pad_value, 6.=first(6., 5., 1.)

import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1], append_batch_size=False,
             dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x)