**Summary:** This commit adds back the functionality to swap
`FakeQuantized*` modules back to the corresponding `torch.nn.*`
without performing post-training quantization:
```
QATConfig(base_config=None, step="convert")
```

This has the exact same functionality as this deprecated config:
```
FromIntXQuantizationAwareTrainingConfig()
```

This functionality is added back since it may be useful to users
who wish to save QAT trained checkpoints from models containing
only `torch.nn.*` modules (not `FakeQuanitzed*` modules), e.g.
when training and inference need to happen on different machines:
```
quantize_(model, QATConfig(base_ptq_config, step="prepare"))
train(model)
quantize_(model, QATConfig(step="convert"))
torch.save(model.state_dict(), "my_checkpoint.pt")

\# On a different machine
model.load_state_dict(torch.load("my_checkpoint.pt"))
quantize_(model, base_ptq_config)
```

**Test Plan:**
```
python test/quantization/test_qat.py  -k qat_config_init
python test/quantization/test_qat.py  -k qat_api_convert_no_quantization
```