feat: support embedding_bag converter (1D input)

[zewenli98]

Description

Support embedding_bag converter. Currently, only 1D input is supported.
schema: https://github.com/pytorch/pytorch/blob/bdecdfd202df3fa25fd9998070fd19fee4b14971/aten/src/ATen/native/native_functions.yaml#L2251
pytorch doc: https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding_bag.html#torch-nn-functional-embedding-bag

Note:

1. We currently only support 1D input because, in PyTorch, offsets is only used when input is 1D. If input is 2D of shape (B, N), it will be treated as B bags (sequences) each of fixed length N, and this will return B values aggregated in a way depending on the mode. offsets is ignored and required to be None in this case. However, according to the schema, offsets is required for input with any dimensions. There's no place describing how it works. There's a discussion in pytorch repo
2. Currently, we expect the arg offsets to be ndarray or torch tensor because we need to access data in it, but offsets could be ITensor in some cases, which cannot be accessed.

Fixes #2345

Type of change

• New feature (non-breaking change which adds functionality)

Checklist:

• My code follows the style guidelines of this project (You can use the linters)
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas and hacks
• I have made corresponding changes to the documentation
• I have added tests to verify my fix or my feature
• New and existing unit tests pass locally with my changes
• I have added the relevant labels to my PR in so that relevant reviewers are notified

[gs-olive]

See comments for suggestions on improved type enforcement. It seems for this specific case, some operators have args[2] as a proper input, so it might be challenging to convert more generally with this numpy restriction.

[zewenli98]

Thanks for the review. The issues above have been addressed!

[gs-olive]

Looks good to me, pending CI!