[RunInference] Raise when a model handler returns fewer predictions than inputs - #40929

Fixes #40928

utils._convert_to_result pairs each batch of inputs with the model's predictions using zip(), which stops at the shorter of the two. When a handler produced fewer predictions than inputs, the leftover inputs got no result and no error, and num_inferences still counted them. That's how #36587 and #40913 went unnoticed, and it still happens with built-in handlers, for example with PyTorch or Keras models that return a tuple of outputs, and with Hugging Face causal LMs that return past_key_values (details in the issue).

This PR:

• makes _convert_to_result raise a ValueError when there are fewer predictions than inputs. The message gives the counts, names any dict output that doesn't have one entry per input, and suggests a fix (an inference function that returns one prediction per input, or inference_args={'use_cache': False} for Hugging Face models);
• leaves extra predictions ignored, as before, so pipelines in which every input already gets a result keep working (for example, tuple outputs with batch size 1);
• fixes three existing tests that only passed because of the truncation. In sklearn_inference_test.py, test_custom_inference_fn used an inference function that returned one prediction for three inputs, and its zip()-based check compared only the first result. In tensorflow_inference_test.py, the two env-var tests mocked the model, so their pipelines produced no results at all. The fakes now return one prediction per input, and the scikit-learn test also checks the number of results;
• adds unit tests for the helper, a RunInference test that checks the affected inputs reach the failure output of with_exception_handling(), and a CHANGES.md entry.

Testing:

• 9 new tests in utils_test.py. The 4 that check the new error fail without this change; the other 5 cover behavior that doesn't change.
• The whole apache_beam/ml test suite with torch, TensorFlow, transformers, ONNX, scikit-learn and XGBoost installed (Python 3.12): 609 passed, against 600 on master. The only differences are the 9 new tests. The tests that can't run locally (they need GCP credentials or Docker) fail the same way on both.
• The real-model cases from the issue: every case that dropped inputs now raises, and the single-output controls still return one result per input.
• ruff, yapf and pyrefly report no issues.

The issue also asks whether RunInference should check the count itself, to cover custom handlers that pair results with their own zip(). I left that out to keep this change to the shared helper. If you'd prefer a warning instead of an error, or the check in RunInference itself, I'm happy to change it.

R: @jrmccluskey