### Proposed new feature or change:
I work with geospatial data that requires tensors with many dimensions. One
challenge I used to have was when I had to implement a `__getitem__` to access
those tensors using a 1D index. One use case is machine learning, when one
needs to feed models sequentially with sub-tensors of the original one. A
simple example is a matrix of shape 2x2 that has the positions `(0, 0)`, `(0,
1)`, `(1, 0)`, `(1, 1)`, and I want to access it via indices `[0, 1, 2, 3]`.
So, when I say `__getitem__(2)`, I want the position `(1, 0)`.
The internals of numpy probably has such information because arrays are usually
stored contiguously in memory, but I couldn't find a way to access this
information.
I came up with this function that calculates tensor indices given the 1D index
and the shape of the tensor:
``` python
from typing import Tuple
import numpy as np
def f(i: int, s: Tuple[int]) -> Tuple[int]:
return (i,) if len(s) == 1 else f(i // s[-1], s[:-1]) + (i % s[-1],)
```
How to use it:
``` python
tensor = np.arange(12).reshape((3, 4))
row_id, col_id = f(i=6, s=(3, 4))
```
Would numpy like to include this method in its code? If yes, I can check how to
submit a PR. If not, I would appreciate an indication of a more suitable
library (`scipy`, maybe `xarray`).
Daniel
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