* 例 1:

x: 包含一级 LoD 信息的 LoDTensor
    x.lod =  [[ 2,           3,                   1 ]]
    x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
    x.dims = [6, 1]

y: None

target_lod: [4, 2]

Output: 包含一级 LoD 信息的 LoDTensor
    out.lod =  [[4,                          2]]
    out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
    out.dims = [6, 1]

* 例 2:

x: 包含一级 LoD 信息的 LoDTensor
    x.lod =  [[2,            3,                   1]]
    x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
    x.dims = [6, 1]

y: 普通 Tensor，不含 LoD 信息
    y.data = [[2, 4]]
    y.dims = [1, 3]

target_lod: 当 y 不为空时，此参数不起作用

Output: 包含一级 LoD 信息的 LoDTensor
    out.lod =  [[2,            4]]
    out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
    out.dims = [6, 1]

* 例 3:

x: 包含一级 LoD 信息的 LoDTensor
    x.lod =  [[2,            3,                   1]]
    x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
    x.dims = [6, 1]

y: 包含二级 LoD 信息的 LoDTensor
    y.lod =  [[2, 2], [2, 2, 1, 1]]
    y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
    y.dims = [6, 1]

target_lod: 当 y 不为空时，此参数不起作用

Output: 包含二级 LoD 信息的 LoDTensor
    out.lod =  [[2, 2], [2, 2, 1, 1]]
    out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
    out.dims = [6, 1]

import paddle.fluid as fluid
import numpy

# Graph Organizing
x = fluid.layers.data(name='x', shape=[6])
y = fluid.layers.data(name='y', shape=[6], lod_level=2)
output = fluid.layers.lod_reset(x=x, y=y)

# Create an executor using CPU as an example
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# Execute
x_tensor = fluid.core.LoDTensor()
x_tensor.set(numpy.ones([6]).astype(numpy.float32), place)
y_ndarray = numpy.ones([6]).astype(numpy.float32)
y_lod = [[2, 2], [2, 2, 1, 1]]
y_tensor = fluid.create_lod_tensor(y_ndarray, y_lod, place)

res, = exe.run(fluid.default_main_program(),
               feed={'x':x_tensor, 'y':y_tensor},
               fetch_list=[output],
               return_numpy=False)
print(res)
# Output Value:
# lod: [[0, 2, 4], [0, 2, 4, 5, 6]]
# dim: 6
# layout: NCHW
# dtype: float
# data: [1 1 1 1 1 1]