Update base for Update on "Implement aten::index | feat(torchlib) (#862)"

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**This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.**

## Understanding `aten::index`

For `arg0` with shape `[7, 3, 4, 5, 6]`
The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to
```
+>  select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0);
+>  slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807);
+>  slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2);
+>  index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]);
```
Here,
- `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]`
- The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]`
None in `indices` are like fillers for dimensions that cannot be removed in the process.

## Gather op reference

- https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather
- https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/

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Co-authored-by: BowenBao <bowbaomicrosoft.com>

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Author: justinchuby