# tensorplay.functional.index_select

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.index_select.html

```python
tensorplay.functional.index_select(input, dim, index, *, out=None) → Tensor
```

Returns a new tensor which indexes the input tensor along dimension
dim using the entries in index which is a LongTensor.

The returned tensor has the same number of dimensions as the original tensor
(input).  The dimth dimension has the same size as the length
of index; other dimensions have the same size as in the original tensor.

> **Note**
>
> The returned tensor does not use the same storage as the original tensor. If out has a different shape than expected, we silently change it to the correct shape, reallocating the underlying storage if necessary.

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the input tensor.

- dim ([int](https://docs.python.org/3/builtins/functions.html#int)) – the dimension in which we index

- index (IntTensor or LongTensor) – the 1-D tensor containing the indices to index

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> x = tensorplay.randn(3, 4)
>>> x
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-0.4664,  0.2647, -0.1228, -1.1068],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> indices = tensorplay.tensor([0, 2])
>>> tensorplay.index_select(x, 0, indices)
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> tensorplay.index_select(x, 1, indices)
tensor([[ 0.1427, -0.5414],
        [-0.4664, -0.1228],
        [-1.1734,  0.7230]])
```