🚀 Feature request

The architecture GPT2ForSequenceClassification was added in #7501 in PyTorch. It would be great to have it in TensorFlow (cf. issues #7622), but it would also be great to have it for other causal models: OpenAI GPT, CTRL, TransfoXL

Currently working on OpenAI GPT: @fmcurti (done)

Below is a list of items to follow to make sure the integration of such an architect

some recordings have low volume so the output can be sometimes really quiet. how about we add a volume bar so we can make the output louder/quieter?

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Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

Bug Report

These tests were run on s390x. s390x is big-endian architecture.

Failure log for helper_test.py

________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________

self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>

    def test_make_tensor(self):  # type: () -> None
    

more details at: allenai/allennlp#2264 (comment)

🐛 Bug

Please reproduce using the BoringModel and post here

Currently, on_batch_start is called along on_train_batch_start during training but doesn't for test and validation.
It results in a different number of function calls betwee

What would you like to be added: As title

Why is this needed: All pruning schedule except AGPPruner only support level, L1, L2. While there are FPGM, APoZ, MeanActivation and Taylor, it would be much better if we can choose any pruner with any pruning schedule.

**Without this feature, how does current nni

Currently our non-notebook examples are manually included in the examples webpage via custom .rst files in tutorial/source/. As the number of examples increases, it would be better to follow NumPyro's approach and generate HTML pages automatically with sphinx_gallery. This would make examples easier t