feat: add batch inference API to llama stack inference (#1945)

# What does this PR do?

This PR adds two methods to the Inference API:
- `batch_completion`
- `batch_chat_completion`

The motivation is for evaluations targeting a local inference engine
(like meta-reference or vllm) where batch APIs provide for a substantial
amount of acceleration.

Why did I not add this to `Api.batch_inference` though? That just
resulted in a _lot_ more book-keeping given the structure of Llama
Stack. Had I done that, I would have needed to create a notion of a
"batch model" resource, setup routing based on that, etc. This does not
sound ideal.

So what's the future of the batch inference API? I am not sure. Maybe we
can keep it for true _asynchronous_ execution. So you can submit
requests, and it can return a Job instance, etc.

## Test Plan

Run meta-reference-gpu using:
```bash
export INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct
export INFERENCE_CHECKPOINT_DIR=../checkpoints/Llama-4-Scout-17B-16E-Instruct-20250331210000
export MODEL_PARALLEL_SIZE=4
export MAX_BATCH_SIZE=32
export MAX_SEQ_LEN=6144

LLAMA_MODELS_DEBUG=1 llama stack run meta-reference-gpu
```

Then run the batch inference test case.

llama_stack/models/llama/llama4/generation.py

@@ -233,7 +233,7 @@ class Llama4:
                         source="output",
                         logprobs=(token_logprobs[idx, cur_pos : cur_pos + 1].tolist() if logprobs else None),
                         batch_idx=idx,
-                        finished=eos_reached[idx],
+                        finished=eos_reached[idx].item(),
                         ignore_token=cur_pos < len(prompt_tokens[idx]),
                     )
                 )