# sequence_expand_as¶

paddle.static.nn. sequence_expand_as ( x, y, name=None ) [source]

Sequence Expand As Layer. This OP will expand the input variable `x` according to the zeroth level lod of `y`. Current implementation requires the level number of `y`’s lod must be 1, and the first dimension of `x` should be equal to the size of `y`’s zeroth level lod, thus the expanded Tensor has the same lod info as `y`. The expanded result has nothing to do with `x`’s lod, so the lod of Input(X) is not considered.

Note

Please note that the input `x` should be Tensor or Tensor, and input `y` must be Tensor.

Following examples will explain how sequence_expand_as works:

```Case 1:

Consider 4 sequences [a], [b], [c], [d], now we want to expand them to [a][a][a], [b][b][b], [c] and [d].
It's obvious that the lod info of expanded sequences is [0, 3, 6, 7, 8].
Given a 1-level Tensor ``x``:
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
and input ``y``
y.lod = [[3, 3, 1, 1]] #lod based on length may be easier to understand

then we get 1-level Tensor out:
Out.lod =  [[0,            3,              6,  7,  8]] #based on offset
Out.data = [[a], [a], [a], [b], [b], [b], [c], [d]]
Out.dims = [8, 1]

Case 2:

Given a common Tensor ``x``:
x.data = [[a, b], [c, d], [e, f]]
x.dims = [3, 2]
and input ``y``:
y.lod = [[0, 2, 3, 6]]

then we get a 1-level Tensor:
out.lod =  [[0,             2,     3,                    6]]
out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
out.dims = [6, 2]
```
Parameters
• x (Tensor) – The input variable which is a Tensor or Tensor, with the dims `[M, K]`. The data type should be float32, float64, int32 or int64.

• y (Tensor) – The input variable which is a Tensor with 1-level lod.

• name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

Returns

Tensor, The expanded variable which is a Tensor with the dims `[N, K]`. `N` depends on the lod of `y`, and the lod level must be 1. The data type is same as input.

Examples

```>>> import paddle
>>> import numpy as np

>>> x = paddle.static.data(name='x', shape=[4, 1], dtype='float32')
>>> y = paddle.static.data(name='y', shape=[8, 1], dtype='float32', lod_level=1)

>>> exe = base.Executor(base.CPUPlace())
>>> place = base.CPUPlace()

>>> np_data = np.array([[1], [2], [3], [4]]).astype('float32')
>>> x_lod_tensor = base.create_lod_tensor(np_data, [[2, 2]], place)
>>> print(x_lod_tensor)
- lod: {{0, 2, 4}}
- place: Place(cpu)
- shape: [4, 1]
- layout: NCHW
- dtype: float32
- data: [1 2 3 4]

>>> np_data = np.array([[1], [2], [3], [4], [5], [6], [7], [8]]).astype('float32')
>>> y_lod_tensor = base.create_lod_tensor(np_data, [[3,3,1,1]], place)
>>> print(y_lod_tensor)
- lod: {{0, 3, 6, 7, 8}}
- place: Place(cpu)
- shape: [8, 1]
- layout: NCHW
- dtype: float32
- data: [1 2 3 4 5 6 7 8]

>>> out_main = exe.run(base.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}}
- place: Place(cpu)
- shape: [8, 1]
- layout: NCHW
- dtype: float32
- data: [1 1 1 2 2 2 3 4]
```