Decorators and generators are important Python features that demonstrate how high-level language constructs are translated into structured runtime behaviour. In the context of Data Structures and Algorithms Lab, understanding their execution mechanics is useful because both features affect how functions, control flow, memory, and iteration are managed during program execution. Although decorators and generators are written using simple Python syntax, modern Python runtime engines such as CPython implement them through function objects, code objects, frames, bytecode instructions, closures, iterators, and suspension mechanisms. A decorator is a mechanism for modifying or extending the behaviour of a function or class without changing its original source-code body. The basic syntax uses the `@` symbol. For example: ```python def decorator(func): def wrapper(): print("Before") func() print("After") return wrapper @decorator def display(): print("Hello") ``` The statement `@decorator` is essentially syntactic shorthand for: ```python display = decorator(display) ``` The important point is that decoration takes place when the `def` statement is executed, rather than every time `display()` is subsequently called. Python first creates a function object representing `display`. It then passes that function object to `decorator`. The decorator returns another callable, normally the `wrapper` function. The name `display` is then rebound to this returned object. Therefore, when `display()` is called later, Python actually invokes the wrapper. At runtime, a Python function is represented by a function object containing information such as its code object, global namespace, default arguments, annotations, and closure information. The code object contains the compiled instructions that describe the function's behaviour. A decorator does not normally modify the original code object directly. Instead, it commonly creates another function object that controls access to the original function. Closures are particularly important in decorator execution. In the example, `wrapper()` refers to `func`, even though `func` is a local variable of the outer `decorator()` function. Python preserves this reference using a closure. The wrapper function contains a reference to the captured value through a closure cell. Consequently, after `decorator()` finishes execution, the original function remains accessible to `wrapper()`. When the decorated function is called, the runtime creates an execution frame for the wrapper. The wrapper executes its instructions, prints `"Before"`, and then performs a function call on the captured `func` object. A separate frame is created for the original function. After the original function returns, control goes back to the wrapper, which executes `"After"` and finally returns. Thus, decorators introduce an additional layer into the call structure. Modern Python implementations compile source code into bytecode before execution. In CPython, bytecode instructions control operations such as loading variables, calling functions, returning values, creating closures, and handling exceptions. The exact bytecode representation changes between Python versions, but the underlying structural concept remains that a decorator is implemented by executing the decorator expression and rebinding the function name to the returned callable. Decorators are useful in algorithmic programs for several purposes. They can measure execution time, perform logging, validate arguments, count function calls, enforce conditions, or implement caching. For example, a timing decorator