add NVIDIA NIM inference adapter (#355)

# What does this PR do?

this PR adds a basic inference adapter to NVIDIA NIMs

what it does -
 - chat completion api
   - tool calls
   - streaming
   - structured output
   - logprobs
 - support hosted NIM on integrate.api.nvidia.com
 - support downloaded NIM containers

what it does not do -
 - completion api
 - embedding api
 - vision models
 - builtin tools
 - have certainty that sampling strategies are correct

## Feature/Issue validation/testing/test plan

`pytest -s -v --providers inference=nvidia
llama_stack/providers/tests/inference/ --env NVIDIA_API_KEY=...`

all tests should pass. there are pydantic v1 warnings.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Did you read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?

Thanks for contributing 🎉!

llama_stack/providers/tests/inference/conftest.py

@@ -6,6 +6,8 @@
 
 import pytest
 
+from ..conftest import get_provider_fixture_overrides
+
 from .fixtures import INFERENCE_FIXTURES
 
 
@@ -67,11 +69,12 @@ def pytest_generate_tests(metafunc):
             indirect=True,
         )
     if "inference_stack" in metafunc.fixturenames:
-        metafunc.parametrize(
-            "inference_stack",
-            [
-                pytest.param(fixture_name, marks=getattr(pytest.mark, fixture_name))
-                for fixture_name in INFERENCE_FIXTURES
-            ],
-            indirect=True,
-        )
+        fixtures = INFERENCE_FIXTURES
+        if filtered_stacks := get_provider_fixture_overrides(
+            metafunc.config,
+            {
+                "inference": INFERENCE_FIXTURES,
+            },
+        ):
+            fixtures = [stack.values[0]["inference"] for stack in filtered_stacks]
+        metafunc.parametrize("inference_stack", fixtures, indirect=True)