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Update RFC-00xx-interpolation.md
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# Interpolation

## Authors

* Allen Goodman (@0x00b1)

## Summary

Interpolation is a technique for adding new data points in a range of a set of known data points. You can use interpolation to fill-in missing data, smooth existing data, make predictions, and more.

Interpolation operators operate on:

* data on a regular grid (i.e., predetermined, not necessarily, uniform, spacing); or
* scattered data on an irregular grid.

### `torch.interpolation.interpolate`

```Python
from typing import Callable, Optional, Tuple

from torch import Tensor

def interpolate(
x: Tuple[Tensor],
v: Tensor,
q: Tuple[Tensor],
f: Callable[[Tuple[Tensor]], Tuple[Tensor]],
*,
out: Optional[Tensor] = None
):
raise NotImplementedError
```

Interpolate $n$-dimensional data on a regular grid (i.e., predetermined, not necessarily, uniform, spacing).

### `torch.interpolation.unstructured_interpolate`

```Python
from typing import Callable, Optional, Tuple

from torch import Tensor

def unstructured_interpolate(
input: Tensor,
points: Tuple[Tensor],
x_i: Tuple[Tensor],
interpolant: Callable[[Tensor], Tensor],
*,
out: Optional[Tensor] = None
):
raise NotImplementedError
```

Interpolate scattered data on an irregular grid.

**Note**—Using this operation in dimensions greater than six is impractical because the memory required by the underlying Delaunay triangulation grows exponentially with its rank.

**Note**—Because this operator uses a Delaunay triangulation, it can be sensitive to scaling issues in `input`. When this occurs, you should standardize `input` to improve the results.

##### Parameters

**input** ([Tensor](https://pytorch.org/docs/stable/tensors.html#torch.Tensor)) –

**points** ([Tensor](https://pytorch.org/docs/stable/tensors.html#torch.Tensor)) –

**x_i** ([Tensor](https://pytorch.org/docs/stable/tensors.html#torch.Tensor)) –

**interpolant** ([Tensor](https://pytorch.org/docs/stable/tensors.html#torch.Tensor)) –

##### Keyword Arguments

**out** ([Tensor](https://pytorch.org/docs/stable/tensors.html#torch.Tensor), *optional*) – output.

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