# tensorplay.functional.empty_permuted

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

```python
tensorplay.functional.empty_permuted(size, physical_layout, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) → Tensor
```

Creates an uninitialized, non-overlapping and dense tensor with the
specified size, with physical_layout specifying how the
dimensions are physically laid out in memory (each logical dimension is listed
from outermost to innermost).  physical_layout is a generalization
of NCHW/NHWC notation: if each dimension is assigned a number according to
what order they occur in size (N=0, C=1, H=2, W=3), then NCHW is (0, 1, 2, 3)
while NHWC is (0, 2, 3, 1).  Equivalently, the strides of the output
tensor t are such that t.stride(physical_layout[i]) == contiguous_strides[i]
(notably, this function is not equivalent to tensorplay.empty(size).permute(physical_layout)).

Unlike tensorplay.empty_strided(), this is guaranteed to produce a dense
tensor with no overlaps.  If possible, prefer using this function over
tensorplay.empty_strided() or manual use of tensorplay.as_strided().

> **Note**
>
> The returned storage is uninitialized, and tensorplay.use_deterministic_algorithms() does not change that. If the contents matter, allocate with tensorplay.zeros() or fill the tensor before reading it, otherwise the output becomes a source of nondeterminism. Floating point and complex tensors are filled with NaN, and integer tensors are filled with the maximum value.

Parameters:

- size ([tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple) of [int](https://docs.python.org/3/builtins/functions.html#int)) – the shape of the output tensor

- physical_layout ([tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple) of [int](https://docs.python.org/3/builtins/functions.html#int)) – the ordering of dimensions physically in memory

Keyword Arguments:

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType), optional) – the desired data type of returned tensor. Default: if None, uses a global default (see [tensorplay.set_default_dtype()](/docs/generated/tensorplay.set_default_dtype.html#tensorplay.set_default_dtype)).

- 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.

Examples

```
>>> tensorplay.empty((2, 3, 5, 7)).stride()
(105, 35, 7, 1)
>>> tensorplay.empty_permuted((2, 3, 5, 7), (0, 1, 2, 3)).stride()
(105, 35, 7, 1)
>>> tensorplay.empty((2, 3, 5, 7), memory_format=tensorplay.channels_last).stride()
(105, 1, 21, 3)
>>> tensorplay.empty_permuted((2, 3, 5, 7), (0, 2, 3, 1)).stride()
(105, 1, 21, 3)
>>> tensorplay.empty_permuted((2, 3, 5, 7), (0, 2, 3, 1)).dim_order()
(0, 2, 3, 1)
```