[chore] log quant config to the user_agent

[sayakpaul]

What does this PR do?

To get finegrained understanding of which quant configs are most used, allow quant configs to also go in user_agent.

Example value:

user_agent['quant_config']='{"modules_to_not_convert": null, "quant_method": "torchao", "quant_type": {"default": {"_data": {"granularity": {"_data": {"dim": -1}, "_type": "PerRow", "_version": 1}, "group_size": null, "set_inductor_config": true}, "_type": "Int8WeightOnlyConfig", "_version": 2}}}'

For:

from diffusers import AutoModel, TorchAoConfig
import torch 
from torchao.quantization import Int8WeightOnlyConfig

model = AutoModel.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    subfolder="transformer",
    torch_dtype=torch.bfloat16,
    quantization_config=TorchAoConfig(Int8WeightOnlyConfig(group_size=None, version=2))
)

Additionally, also pass the model class to user_agent to better understand which quantization classes are usually used for a particular class of models.

[HuggingFaceDocBuilderDev]

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