1/23/2024 0 Comments Python unpacking arguments![]() Use Python *arg arguments for a function that accepts a variable number of arguments.When you precede the argument a with the operator *, Python unpacks the tuple and assigns its elements to x and y parameters. To fix this, you need to prefix the tuple a with the operator * like this: def point(x, y): If you pass a tuple to the point function, you’ll get an error: a = (0, 0)Įrror: TypeError: point() missing 1 required positional argument: 'y' Code language: JavaScript ( javascript ) Return f '()' Code language: JavaScript ( javascript ) The following point function accepts two arguments and returns a string representation of a point with x-coordinate and y-coordinate: def point(x, y): In this example, Python assigns 10 to x, 20 to y, (30,40) to args, and 50 to z. To fix it, you need to use a keyword argument after the *args argument as follows: def add(x, y, *args, z): The following example results in an error because it uses a positional argument after the *arg argument: def add(x, y, *args, z):Įrror: TypeError: add() missing 1 required keyword-only argument: 'z' Code language: JavaScript ( javascript ) If you use the *args argument, you cannot add more positional arguments. Output: 6 Code language: Python ( python ) Python *args argument exhausts positional arguments Print(total) Code language: Python ( python ) The following shows how to add all numbers of the args tuple in the add() function: def add (*args): To access each element of the args argument, you use the square bracket notation with an index: def add (*args):Īlso, you an use a for loop to iterate over the elements of the tuple. Now, the args has three numbers 1, 2, and 3. Output: ( 1, 2, 3) Code language: Python ( python ) The following passes three arguments to the add() function: def add (*args):Īdd( 1, 2, 3) Code language: Python ( python ) Since we didn’t pass any argument to the add() function, the output shows an empty tuple. The following shows the type of the args argument and its contents: def add (*args): Output: () Code language: Python ( python ) Let’s take a look at the following example: def add (*args): However, by convention, Python uses the *args for a variadic parameter. For example, you can use any meaningful names like *numbers, *strings, *lists, etc. Note that you don’t need to name args for a variadic parameter. Functions that have variadic parameters are called variadic functions. ![]() In Python, the parameters like *args are called variadic parameters. And you can pass zero, one, or more arguments to the *args parameter. When a function has a parameter preceded by an asterisk ( *), it can accept a variable number of arguments. Introduction to the Python *args parameter It’s like tuple unpacking except that the args is a tuple, not a list. When passing the positional arguments 10, 20, 30, and 40 to the function, Python assigns 10 to x, 20 to y, and a tuple (30, 40) to args. The *args is a special argument preceded by a star ( *). The add function accepts three parameters x, y, and *args. Python uses the same concept for the function arguments. Similarly, the following assigns 10 to x, 20 to y, and the list to z: x, y, *z = 10, 20, 30, 40 print(x) In this example, Python passed 10 to x and 20 to y. It’s similar to passing two arguments to a function: def add(x, y): The following unpacks a tuple into two variables: x, y = 10, 20 I actually opened a feature request a while back for adding support for resolving Ray objects arbitrarily nested in task argument data structures.Summary: in this tutorial, you’ll learn about the Python *args parameters and how to use them for defining variadic functions. Meanwhile, in wrapper2.remote(jobB, *inputs), ob_A1 and ob_A2 are each top-level arguments to the wrapper2 task, so those Ray objects are resolved to their concrete values as expected. In other words, when resolving Ray object task arguments, Ray does not traverse arbitrary data structures in the task arguments to find and resolve those Ray objects and repackage them into the original argument data structures when the task executes, and instead expects any Ray objects that you wish to be automatically resolved to be top-level arguments. Wrapper.remote(jobB, inputs) doesn’t work because only Ray objects that are top-level task arguments will get automatically resolved to their underlying values, and given that inputs is a tuple of Ray objects, ob_A1 and ob_A2 will not get resolved. I am unable to explain this behaviour (maybe it is related to python, and not ray). However with jobB, wrapper() gives an error saying that it cannot add two ray objects. ![]() Now there are two ways to pass the inputs as shown by wrapper and wrapper2. The wrapper takes the function to be executed and its inputs as arguments. To illustrate, here is a toy example which intends to write a wrapper to run any function on remote. ![]() Hi, I was using Ray for a project and encountered this problem.
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