Always use proper nullable type annotations and safe access patterns to prevent runtime errors from null/undefined values. Use Optional[Type] or Union[None, Type] for nullable parameters, employ safe dictionary access with .get(), and handle None values explicitly in operations.
Examples of good practices:
# Use Optional type annotations
def process_data(encoder_lens: Optional[torch.Tensor] = None):
pass
# Safe dictionary access
draft_url = params.get("draft_url", None) # Instead of params["draft_url"]
# Null-safe assertions
assert (topk_weights is None) or (topk_weights.shape == topk_ids_.shape)
This prevents KeyError exceptions, improves type safety, and makes null handling explicit rather than implicit. Always consider whether a parameter or variable can be None and handle it appropriately in both type annotations and runtime logic.
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