from multiprocessing import Pool
# Function to process each item in the iterable\
def process_data(sdg):
# Process your data here
mydata = self.get_id(sdg, n_variable, _del, new_split[1])
return mydata
if __name__ == '__main__':
# Create a pool of worker processe
pool = Pool()  # By default, it will use the number of CPU cores available

# Distribute the work among the workers and retrieve the results
results = pool.map(process_data, sds)  # sds is your iterable
# Process the results as needed
for result in results: