# tile¶

paddle. tile ( x, repeat_times, name=None ) [source]

Construct a new Tensor by repeating `x` the number of times given by `repeat_times`. After tiling, the value of the i’th dimension of the output is equal to `x.shape[i]*repeat_times[i]`.

Both the number of dimensions of `x` and the number of elements in `repeat_times` should be less than or equal to 6.

Parameters
• x (Tensor) – The input tensor, its data type should be bool, float32, float64, int32 or int64.

• repeat_times (list|tuple|Tensor) – The number of repeating times. If repeat_times is a list or tuple, all its elements should be integers or 1-D Tensors with the data type int32. If repeat_times is a Tensor, it should be an 1-D Tensor with the data type int32.

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

Returns

N-D Tensor. The data type is the same as `x`. The size of the i-th dimension is equal to `x[i] * repeat_times[i]`.

Examples

```import paddle

data = paddle.to_tensor([1, 2, 3], dtype='int32')
out = paddle.tile(data, repeat_times=[2, 1])
print(out)
# Tensor(shape=[2, 3], dtype=int32, place=Place(gpu:0), stop_gradient=True,
#        [[1, 2, 3],
#         [1, 2, 3]])

out = paddle.tile(data, repeat_times=(2, 2))
print(out)
# Tensor(shape=[2, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True,
#        [[1, 2, 3, 1, 2, 3],
#         [1, 2, 3, 1, 2, 3]])

repeat_times = paddle.to_tensor([1, 2], dtype='int32')
out = paddle.tile(data, repeat_times=repeat_times)
print(out)
# Tensor(shape=[1, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True,
#        [[1, 2, 3, 1, 2, 3]])
```