Fix several bugs & add Bert inc example to pipeline

[xiaoyu-work]

Describe your changes

Fix Olive bugs & Add Bert inc examples to example pipeline.

• batch_size is needed for Inc dataloader. Set default size to 1.
• Custom eval func doesn't have batch_size as input.
• For QuantizationAwareTraining pass, train_data_config is not required if user provides training_loop_func.
• Latest transformers package will automatically save trained model as safetensors format. Add save_safetensors as false to train argument.
• Some passes may have nested data_config in its config. Update auto-fill data_config logic to achieve this.

Checklist before requesting a review

• Add unit tests for this change.
• Make sure all tests can pass.
• Update documents if necessary.
• Lint and apply fixes to your code by running lintrunner -a
• Is this a user-facing change? If yes, give a description of this change to be included in the release notes.
• Is this PR including examples changes? If yes, please remember to update example documentation in a follow-up PR.

(Optional) Issue link

[jambayk]

is this recursion for cases like in inc quantization where it has a metric with data config inside it?

[jambayk]

not an issue but I think the recursion feels more correct if the order of logic is

if param_name.endswith("data_config"):
     _resolve_data_config(config, values, param_name)
elif isinstance(param_value, dict):
    resolve_all_data_configs(param_value, values)

since now there is an explicit base case

[xiaoyu-work]

Sounds good! Updated.