Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@src/aiperf/common/hash_id_random_generator.py`:
- Around line 46-49: The truthiness check in HashIdRandomGenerator.from_base_rng
incorrectly treats seed 0 as falsy; change the logic to preserve an explicit
seed of 0 by testing for None instead of using `or`. Specifically, in
from_base_rng use a None check on base_rng.seed (e.g., base_seed = base_rng.seed
if base_rng.seed is not None else base_rng.randrange(...)) so that a seed value
of 0 is kept; this references the from_base_rng method and the base_rng.seed
attribute on RandomGenerator.

In `@src/aiperf/common/tokenizer.py`:
- Around line 51-56: The helper _supports_kwarg currently calls
inspect.signature(method) without guarding for introspection failures; update
_supports_kwarg to catch exceptions like ValueError and TypeError raised by
inspect.signature and return False in those cases so non-introspectable
tokenizer methods are treated the same as missing methods (i.e., no kwarg
support). Locate the _supports_kwarg function and wrap the
inspect.signature(method).parameters access in a try/except that returns False
on ValueError/TypeError, keeping existing behavior for missing methods.

In `@src/aiperf/dataset/dataset_manager.py`:
- Around line 159-160: The Kubernetes-mode log message in dataset_manager.py
builds an f-string containing self._conversation_count and currently ends with
an extra closing parenthesis in the literal "backing store)"; update the string
used in the logging call (the f"...Kubernetes mode: skipped local client,
compressed {self._conversation_count} conversations into backing store)") to
remove the stray ')' so the message reads without the extra parenthesis.

In `@src/aiperf/dataset/generator/prompt.py`:
- Around line 289-299: The cache key uses only hash_id which breaks when
current_block_size varies; change the caching to include current_block_size
(e.g., use a tuple key like (hash_id, current_block_size)) so that self._cache
stores size-specific samples; update any annotations for self._cache
accordingly, ensure you still call
self._hash_id_corpus_rng.reseed_for_hash_id(hash_id) and populate the cache
using sample_tokens_from_corpus(self._tokenized_corpus, current_block_size,
self._hash_id_corpus_rng, self.tokenizer.block_separation_token_id), and
continue to extend final_prompt with the cached value.

In `@src/aiperf/dataset/loader/parallel_convert.py`:
- Around line 138-146: Validate that final_block_size is positive before using
it: compute final_block_size as currently done (in the block handling hash_ids
inside parallel_convert.py), then check that input_length >= (len(hash_ids) - 1)
* block_size (or equivalently final_block_size > 0) and raise a clear ValueError
(or return/skip the trace) if the check fails; reference the variables hash_ids,
input_length, final_block_size and the functions get_block_tokens/decode so
maintainers can locate where to add the validation and the error message
describing the invalid sizes (include values for input_length, len(hash_ids),
and block_size in the error).

In `@tests/unit/dataset/loader/test_base_trace_loader.py`:
- Around line 38-72: Add explicit type annotations to all untyped fixtures and
test functions in this file: annotate create_jsonl_file with its
return/generator type (e.g., Iterator[Callable[[List[str]], str]]), mark
default_user_config() -> UserConfig, mock_prompt_generator() -> Mock (or
PromptGenerator protocol), and add -> None return annotations to all test_*
functions and typed parameters where applicable; update any local variables'
types if needed to satisfy type checkers (references: create_jsonl_file,
default_user_config, mock_prompt_generator, and all test_* functions).
- Around line 422-442: The test test_turn_fields_populated is missing an
assertion for the turn.delay field; update the test to assert that turn.delay
equals the expected value produced by MooncakeTraceDatasetLoader (i.e., the
delay mapping implemented in MooncakeTraceDatasetLoader.convert_to_conversations
or loader.load_dataset), for example add a line like assert turn.delay ==
<expected_delay> where <expected_delay> is the loader's computed/default delay
for the provided record (commonly 0 if no delay field is present).

---

Outside diff comments:
In `@src/aiperf/dataset/protocols.py`:
- Line 20: Update the protocol docstrings to reflect the new iterable return
contract: change any wording that says “list of Conversation objects” to
“Iterable[Conversation]” and explicitly note that callers should not assume list
ops like len() or indexing are available; update the main protocol docstring
(the one starting “Protocol for custom dataset loaders...”) and the related
docblock referenced around lines 49–51 to match this wording and mention that
callers should consume the returned Iterable via iteration or by materializing
it into a list if random access is required.

---

Nitpick comments:
In `@tests/unit/common/test_hash_id_random_generator.py`:
- Around line 15-22: The RNG setup/reset fixture is duplicated across test
classes; keep a single module-level autouse fixture instead: define the existing
setup_rng fixture once at the top of the test module (using
`@pytest.fixture`(autouse=True)) that calls rng.reset(), rng.init(42), yield, then
rng.reset(), and remove the duplicate fixture definitions (e.g., the other
occurrence around lines 215-222); keep references to the same symbols
(setup_rng, rng.reset, rng.init) so all tests get the shared setup.

In `@tests/unit/dataset/loader/test_base_trace_loader.py`:
- Around line 243-348: Tests test_large_dataset_uses_parallel,
test_exactly_threshold_uses_parallel, and
test_below_threshold_uses_single_threaded are duplicate threshold variations and
should be consolidated into one pytest.mark.parametrize test; create a single
test (e.g., test_threshold_dispatch_parametrized) that takes a trace_count and
expected_parallel boolean, build content using _MIN_TRACES_FOR_PARALLEL +/-
values, instantiate MooncakeTraceDatasetLoader as before, call
loader.load_dataset() and then patch
aiperf.dataset.loader.base_trace_loader.parallel_convert and call
list(loader.convert_to_conversations(data)), finally assert
mock_parallel.assert_called_once() when expected_parallel is True or
mock_parallel.assert_not_called() when False; keep _MIN_TRACES_FOR_PARALLEL,
MooncakeTraceDatasetLoader, convert_to_conversations, and parallel_convert
references to locate code.

In `@tests/unit/dataset/loader/test_parallel_convert.py`:
- Around line 481-522: The test test_init_worker_sets_up_state contains an
unused fixture parameter tmp_path; remove tmp_path from the test signature so
the test becomes def test_init_worker_sets_up_state(self, sample_corpus_array):
and update any references if present, leaving the body that creates
shared_memory, constructs _WorkerInitArgs, calls _init_worker, and asserts on
parallel_convert_mod._worker_state unchanged; ensure the test imports/mocks
(e.g., Tokenizer.from_pretrained) and the cleanup resetting
parallel_convert_mod._worker_state and closing/unlinking shm remain intact.

In `@tests/unit/dataset/test_dataset_manager.py`:
- Around line 465-466: Locate the two placeholder comments "Verify in-memory
dataset was not materialized" in tests/unit/dataset/test_dataset_manager.py and
either remove them or replace them with concrete assertions: call the test's
DatasetManager instance (e.g., dataset_manager) and assert the dataset is not
marked materialized (e.g., assert not
dataset_manager.is_materialized(dataset_id)) or assert no materialized file/path
exists on disk (e.g., assert not os.path.exists(materialized_path)); if the
project uses an internal map (e.g., dataset_manager._materialized or
dataset_manager.materialized_datasets), assert the dataset_id is not present
there instead. Ensure the chosen assertion uses existing public
methods/attributes in the test context (materialize_dataset, is_materialized,
materialized_datasets, or filesystem path helpers) so the verification is real
and not just a comment.