# tensorplay.functional.randperm

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

```python
tensorplay.functional.randperm(n, *, generator=None, out=None, dtype=tensorplay.int64, layout=tensorplay.strided, device=None, requires_grad=False, pin_memory=False) → Tensor
```

Returns a random permutation of integers from 0 to n - 1.

Parameters:

n ([int](https://docs.python.org/3/builtins/functions.html#int)) – the upper bound (exclusive)

Keyword Arguments:

- generator ([tensorplay.Generator](/docs/generated/tensorplay.Generator.html#tensorplay.Generator), optional) – a pseudorandom number generator for sampling

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

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType), optional) – the desired data type of returned tensor. Default: tensorplay.int64.

- layout (tensorplay.layout, optional) – the desired layout of returned Tensor. Default: tensorplay.strided.

- device ([tensorplay.device](/docs/generated/tensorplay.Device.html#tensorplay.Device), optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see [tensorplay.set_default_device()](/docs/generated/tensorplay.set_default_device.html#tensorplay.set_default_device)). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

- requires_grad ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – If autograd should record operations on the returned tensor. Default: False.

- pin_memory ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

Example:

```
>>> tensorplay.randperm(4)
tensor([2, 1, 0, 3])
```