[Example] fixing TGN example

[amarzullo24]

Description

Attempt to fix the example: Temporal Graph Network (TGN)
refer to #5452 #3536 #5456

Checklist

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [$CATEGORY] (such as [NN], [Model], [Doc], [Feature]])
• I've leverage the tools to beautify the python and c++ code.
• The PR is complete and small, read the Google eng practice (CL equals to PR) to understand more about small PR. In DGL, we consider PRs with less than 200 lines of core code change are small (example, test and documentation could be exempted).
• All changes have test coverage
• Code is well-documented
• To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
• Related issue is referred in this PR
• If the PR is for a new model/paper, I've updated the example index here.

Changes

TGN example was removed due to compatibility issues with recent DGL versions (https://github.com/dmlc/dgl/blob/master/examples/pytorch/tgn)
This PR contains simple fixes to adapt the dataloader

Tested by running in fast mode:

python train.py --dataset wikipedia --fast_mode

Obtained performance is comparable with the one reported in the previous version:

Batch:  462 Time:  0.11104059219360352
Batch:  463 Time:  0.13099265098571777
Batch:  464 Time:  0.11907100677490234
Batch:  465 Time:  0.11619687080383301
Batch:  466 Time:  0.11682605743408203
Batch:  467 Time:  0.12520647048950195
Batch:  468 Time:  0.10740518569946289
Batch:  469 Time:  0.11810636520385742
Batch:  470 Time:  0.033731937408447266
Epoch: 49; Training Loss: 28394.831434637308 | Validation AP: 0.985 AUC: 0.987
 Epoch: 49; Test AP: 0.983 AUC: 0.987
 Epoch: 49; Test New Node AP: 0.980 AUC: 0.983

========Training is Done========

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