# stack¶

paddle. stack ( x, axis=0, name=None ) [source]

Stacks all the input tensors `x` along `axis` dimension. All tensors must be of the same shape and same dtype.

For example, given N tensors of shape [A, B], if `axis == 0`, the shape of stacked tensor is [N, A, B]; if `axis == 1`, the shape of stacked tensor is [A, N, B], etc.

```Case 1:

Input:
x[0].shape = [1, 2]
x[0].data = [ [1.0 , 2.0 ] ]
x[1].shape = [1, 2]
x[1].data = [ [3.0 , 4.0 ] ]
x[2].shape = [1, 2]
x[2].data = [ [5.0 , 6.0 ] ]

Attrs:
axis = 0

Output:
Out.dims = [3, 1, 2]
Out.data =[ [ [1.0, 2.0] ],
[ [3.0, 4.0] ],
[ [5.0, 6.0] ] ]

Case 2:

Input:
x[0].shape = [1, 2]
x[0].data = [ [1.0 , 2.0 ] ]
x[1].shape = [1, 2]
x[1].data = [ [3.0 , 4.0 ] ]
x[2].shape = [1, 2]
x[2].data = [ [5.0 , 6.0 ] ]

Attrs:
axis = 1 or axis = -2  # If axis = -2, axis = axis+ndim(x[0])+1 = -2+2+1 = 1.

Output:
Out.shape = [1, 3, 2]
Out.data =[ [ [1.0, 2.0]
[3.0, 4.0]
[5.0, 6.0] ] ]
```
Parameters
• x (list[Tensor]|tuple[Tensor]) – Input `x` can be a `list` or `tuple` of tensors, the Tensors in `x` must be of the same shape and dtype. Supported data types: float32, float64, int32, int64.

• axis (int, optional) – The axis along which all inputs are stacked. `axis` range is `[-(R+1), R+1)`, where `R` is the number of dimensions of the first input tensor `x[0]`. If `axis < 0`, `axis = axis+R+1`. The default value of axis is 0.

• name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Tensor, The stacked tensor with same data type as input.

Examples

```>>> import paddle

>>> out = paddle.stack([x1, x2, x3], axis=0)
>>> print(out.shape)
[3, 1, 2]
>>> print(out)
Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1., 2.]],
[[3., 4.]],
[[5., 6.]]])

>>> out = paddle.stack([x1, x2, x3], axis=-2)
>>> print(out.shape)
[1, 3, 2]
>>> print(out)
Tensor(shape=[1, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1., 2.],
[3., 4.],
[5., 6.]]])
```