Improving the HARD_GAT example

[drivanov]

Description

In the current PR we eliminate the following warnings:

/usr/local/lib/python3.10/dist-packages/dgl/data/utils.py:354: UserWarning: Property dataset.num_labels will be deprecated, please use dataset.num_classes instead.

/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,

/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

and few nan outputs:

Epoch 00000 | Time(s) nan | Loss 1.7912 | TrainAcc 0.1917 | ValAcc 0.5440 | ETputs(KTEPS) nan
Epoch 00001 | Time(s) nan | Loss 1.7897 | TrainAcc 0.2417 | ValAcc 0.6600 | ETputs(KTEPS) nan
Epoch 00002 | Time(s) nan | Loss 1.7857 | TrainAcc 0.4667 | ValAcc 0.6960 | ETputs(KTEPS) nan

Current PR is quite similar to #5999,

The average duration of the epoch calculations, which was previously done with np.mean(dur), is now done recursively.

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

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Lint passed, the example is not covered by CI, so merge without waiting for CI complete.