例1:
假设，有4个长度维1的序列[a]、[b]、[c]和[d]，现在要将其扩展为长度是3、3、1、1的序列[a][a][a]、[b][b][b]、[c]和[d]。
显然，扩展后的序列lod为[0, 3, 6, 7, 8]，则：
给定输入一维LoDTensor x
    x.data = [[a], [b], [c], [d]]
    x.dims = [4, 1]
和输入 y
    y.lod = [[3, 3, 1, 1]]    #为了便于理解这里用基于长度lod表示

经过sequence_expand_as运算，得到输出1级LoDTensor out
    out.lod =  [[0,            3,              6,  7,  8]]    #基于偏移的lod，等价于基于长度的[[3, 3, 1, 1]]
    out.data = [[a], [a], [a], [b], [b], [b], [c], [d]]
    out.dims = [8, 1]

可见，输出out将x扩展至和y具有相同的lod。

例2：
设定与例1类似，给定输入一维LoDTensor x：
    x.data = [[a, b], [c, d], [e, f]]
    x.dims = [3, 2]
和输入 y：
    y.lod = [[2, 1, 3]]    #为了便于理解这里用基于长度lod表示

输出为1级LoDTensor：
    out.lod =  [[0,             2,     3,                    6]]    #基于偏移的lod，等价于基于长度的[[2, 1, 3]]
    out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
    out.dims = [6, 2]

可见，输出out将x扩展至和y具有相同的lod。

import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np

x = fluid.data(name='x', shape=[1], dtype='float32')
y = fluid.data(name='y', shape=[1], dtype='float32', lod_level=1)
out = layers.sequence_expand_as(x=x, y=y)

exe = fluid.Executor(fluid.CPUPlace())
place = fluid.CPUPlace()

np_data = np.array([[1], [2], [3], [4]]).astype('float32')
x_lod_tensor = fluid.create_lod_tensor(np_data, [[2, 2]], place)
print(x_lod_tensor)
#lod: [[0, 2, 4]]
#    dim: 4, 1
#    layout: NCHW
#    dtype: float
#    data: [1 2 3 4]

y_lod_tensor = fluid.create_random_int_lodtensor([[3,3,1,1]], [1],
                                                place, low=0, high=1)
print(y_lod_tensor)
#lod: [[0, 3, 6, 7, 8]]
#    dim: 8, 1
#    layout: NCHW
#    dtype: int64_t
#    data: [0 0 1 0 1 1 1 0]

out_main = exe.run(fluid.default_main_program(),
                  feed={'x': x_lod_tensor, 'y': y_lod_tensor},
                  fetch_list=[out], return_numpy=False)
print(out_main[0])
#lod: [[0, 3, 6, 7, 8]]
#    dim: 8, 1
#    layout: NCHW
#    dtype: float
#    data: [1 1 1 2 2 2 3 4]