When working with multiple sequences that need to be combined, prefer higher-level iteration abstractions over nested loops. This improves code readability, reduces nesting depth, and helps prevent logical errors when managing multiple iterative dimensions. For example, instead of writing nested loops: ```python dtypes = [torch.int, torch.long, torch.short] for count_dtype in dtypes: for prob_dtype in dtypes: # process with count_dtype and prob_dtype ``` Use `itertools.product` for a cleaner approach: ```python dtypes = [torch.int, torch.long, torch.short] for count_dtype, prob_dtype in itertools.product(dtypes, repeat=2): # process with count_dtype and prob_dtype ``` Similarly, other Python constructs can simplify iteration patterns: - Use `enumerate` when you need both index and value - Use `zip` to iterate through multiple sequences in parallel - Use comprehensions instead of building collections with loops - Consider `reversed`, `sorted`, or other builtin functions when applicable These higher-level abstractions make algorithmic intent more evident and reduce opportunities for off-by-one errors or incorrect nested logic.