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A decorator factory that creates a decorator to validate function input arguments against a provided schema. Currently a placeholder implementation that passes through without validation.

Purpose

This decorator is designed to provide input validation for functions by checking arguments against a schema before execution. It's intended to ensure data integrity and type safety at function boundaries. The current implementation is a placeholder that returns the original function result without performing validation, indicating this is a framework for future validation logic.

Source Code

def validate_input(schema):
    """
    Decorator to validate function input against schema.
    This is a placeholder for future implementation.
    
    Args:
        schema: Schema to validate against
    """
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            # Future: Validate input against schema
            return func(*args, **kwargs)
        return wrapper
    return decorator

Parameters

Parameter Details

schema: A schema definition object used to validate function inputs. The exact format and type are not specified in the current implementation, but it would typically be a dictionary, class, or validation schema object (e.g., from libraries like jsonschema, pydantic, or marshmallow) that defines expected parameter types, constraints, and validation rules.

Return Value

Returns a decorator function that wraps the target function. The decorator preserves the original function's signature and metadata using functools.wraps. When called, the wrapped function returns the same output as the original function would, as no validation or transformation is currently applied.

Dependencies

• functools

Required Imports

from functools import wraps

Usage Example

from functools import wraps

def validate_input(schema):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            return func(*args, **kwargs)
        return wrapper
    return decorator

# Define a schema (format depends on future implementation)
user_schema = {
    'name': str,
    'age': int,
    'email': str
}

# Apply decorator to function
@validate_input(user_schema)
def create_user(name, age, email):
    return {'name': name, 'age': age, 'email': email}

# Use the decorated function
result = create_user('John Doe', 30, 'john@example.com')
print(result)  # {'name': 'John Doe', 'age': 30, 'email': 'john@example.com'}

Best Practices

• This is a placeholder implementation - do not rely on it for actual validation in production code
• When implementing validation logic, ensure schema validation errors are raised with clear messages
• Consider using established validation libraries (jsonschema, pydantic, marshmallow) rather than custom validation
• The decorator should preserve function metadata using @wraps to maintain introspection capabilities
• Validation should occur before function execution to fail fast and provide early feedback
• Consider logging validation failures for debugging and monitoring purposes
• Document the expected schema format clearly when implementing the actual validation logic
• Handle both positional and keyword arguments appropriately in validation logic

Tags

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