move DataSchemaValidatorMixin into standalone utils (#720)

# What does this PR do?

- there's no value in keeping data schema validation logic in a
DataSchemaValidatorMixin
- move into data schema validation logic into standalone utils

## Test Plan
```
pytest -v -s -m llm_as_judge_scoring_together_inference scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct
pytest -v -s -m basic_scoring_together_inference scoring/test_scoring.py
pytest -v -s -m braintrust_scoring_together_inference scoring/test_scoring.py

pytest -v -s -m meta_reference_eval_together_inference eval/test_eval.py
pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio eval/test_eval.py
```



## Sources

Please link relevant resources if necessary.


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

llama_stack/providers/inline/scoring/basic/scoring.py

@@ -18,8 +18,8 @@ from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
 from llama_stack.distribution.datatypes import Api
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
 from llama_stack.providers.utils.common.data_schema_validator import (
-    DataSchemaValidatorMixin,
     get_valid_schemas,
+    validate_dataset_schema,
 )
 from .config import BasicScoringConfig
 from .scoring_fn.equality_scoring_fn import EqualityScoringFn
@@ -30,7 +30,8 @@ FIXED_FNS = [EqualityScoringFn, SubsetOfScoringFn, RegexParserScoringFn]
 
 
 class BasicScoringImpl(
-    Scoring, ScoringFunctionsProtocolPrivate, DataSchemaValidatorMixin
+    Scoring,
+    ScoringFunctionsProtocolPrivate,
 ):
     def __init__(
         self,
@@ -75,7 +76,7 @@ class BasicScoringImpl(
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse:
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        self.validate_dataset_schema(
+        validate_dataset_schema(
             dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
         )