… schema validation with type enforcement, clearer errors, and updated docstringsProblem:Previously, the `function_schema` function silently defaulted unannotated parameters to `Any`, leading to:- Ambiguous JSON schemas (`"type": "any"`)- Potential runtime errors due to invalid inputs- Reduced reliability of LLM tool callsThis made it harder to ensure type safety without manual validation.Solution:This PR introduces the `enforce_type_annotations` flag to `function_schema`, allowing developers to:1. Enforce type annotations by setting `enforce_type_annotations=True`, which raises a `ValueError` for unannotated parameters.2. Maintain backward compatibility by defaulting to `Any` when `enforce_type_annotations=False` (default).Example error message:```pythonraise ValueError(    f"Parameter '{name}' must be type-annotated. Example: def func({name}: str)")```Changes Made1. New Parameter:```python   def function_schema(       func: Callable[..., Any],       enforce_type_annotations: bool = False,  # New parameter       ...   ):```2. Validation Logic:- If `enforce_type_annotations=True`, unannotated parameters now raise a `ValueError` with a clear example.- Falls back to `Any` if disabled (default behavior preserved).3. Docstring Updates:- Added detailed documentation for `enforce_type_annotations`:```python     Args:         enforce_type_annotations: If True, raises a ValueError for any unannotated parameters.             If False (default), unannotated parameters are assumed to be of type `Any`.```4. Error Message Improvements:- Clear guidance for developers:`Example: def func(x: str)`Backward Compatibility:- Preserved: Existing code continues to work as-is (default `enforce_type_annotations=False`).- Opt-in: Type annotation enforcement is optional, allowing gradual adoption.Testing Guidance:To verify the change:```pythondef test_enforce_type_annotations():    def func(x): ...    with pytest.raises(ValueError):        function_schema(func, enforce_type_annotations=True)    # Should not raise    function_schema(func, enforce_type_annotations=False)```Impact:- Type Safety: Prevents ambiguous schemas and improves LLM tool reliability.- Developer Experience: Clear error messages guide users to fix missing annotations.- Flexibility: Maintains backward compatibility while enabling stricter validation.Example Usage:```python# Strict mode: raises error for unannotated paramsschema = function_schema(my_func, enforce_type_annotations=True)# Default mode: works as beforeschema = function_schema(my_func)  # silently defaults to Any```

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Adds a new enforce_type_annotations flag to function_schema to improve type safety:- When enabled (True): Raises an error for unannotated parameters (e.g., def func(x): ...).- When disabled (False): Falls back to Any (default behavior remains unchanged).- Backward compatibility: Positional arguments still work (e.g., function_schema(my_func, "sphinx")).Example Error Message:Parameter 'x' must be type-annotated. Example: def func(x: str)Test Coverage:https://github.com/openai/openai-agents-python/blob/main/tests/test_function_schema.py

Adds a new enforce_type_annotations flag to function_schema to improve type safety:- When enabled (True): Raises an error for unannotated parameters (e.g., def func(x): ...).- When disabled (False): Falls back to Any (default behavior remains unchanged).- Backward compatibility: Positional arguments still work (e.g., function_schema(my_func, "sphinx")).Example Error Message:Parameter 'x' must be type-annotated. Example: def func(x: str)Test Coverage:https://github.com/openai/openai-agents-python/blob/main/tests/test_function_schema.py

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