How can Python utilize parallel processing to execute a function that takes in one iterable and multiple data inputs, possibly using the multiprocessing starmap technique?

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Python can utilize parallel processing to execute a function that takes in one iterable and multiple data inputs by using the multiprocessing starmap technique, which is a variant of the map function that supports multiple arguments. Here is an example of how this can be done:

import multiprocessing

def my_func(arg1, arg2):
    # some function that takes in two arguments
    ...

if __name__ == '__main__':
    inputs = [(1, 'a'), (2, 'b'), (3, 'c')]
    with multiprocessing.Pool() as pool:
        results = pool.starmap(my_func, inputs)

In this example, the my_func function takes in two arguments arg1 and arg2. The inputs list contains tuples of two values that will be used as arguments for my_func.

The multiprocessing.Pool() object is used to create a pool of worker processes to execute the function in parallel. The pool.starmap() method is then called with the function to execute (my_func) and the inputs to pass in as arguments (inputs).

The results object will contain the results of the function calls in the same order as the inputs. The number of processes created in the pool is determined by the number of available CPU cores on the machine.