Update on "Add convert path for quantize_ QAT API"

Summary: #1415 added a quantize_
QAT API for the prepare path. This commit adds the remaining
convert path for users to actually perform end-to-end QAT using
the quantize_ API. The new flow will look like:

```
from torchao.quantization import (
    quantize_,
    int8_dynamic_activation_int4_weight,
)
from torchao.quantization.qat import (
    FakeQuantizeConfig,
    from_intx_quantization_aware_training,
    intx_quantization_aware_training,
)

activation_config = FakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
weight_config = FakeQuantizeConfig(torch.int4, group_size=32)
quantize_(
    my_model,
    intx_quantization_aware_training(activation_config, weight_config),
)

quantize_(my_model, from_intx_quantization_aware_training())
quantize_(my_model, int8_dynamic_activation_int4_weight(group_size=32))
```

Test Plan:
python test/quantization/test_qat.py -k test_quantize_api_convert_path

[ghstack-poisoned]