code: def process_task(args, task_name, ngrams):
      print(
          " reading from {} and computing ngrams".format(
              "import datasets"
          )
      )
      print(
          " Current entities in ngrams {}".format(len(ngrams)),
          flush=True,
      )
      # using validation/test data from datasets
      from datasets import load_dataset
  
      entities_in_ngrams = len(ngrams)
  
      # load the dataset
      if task_name == "squad":
          dataset = load_dataset("squad_v2", split="validation")
      elif task_name == "natural_questions":
          dataset = load_dataset(
              "natural_questions", split="validation"
          )
      elif task_name == "triviaqa":
          dataset = load_dataset(
              "trivia_qa", "unfiltered", split="test"
          )
      elif task_name == "webqa":
          dataset = load_dataset("web_questions", split="test")
      elif task_name == "race":
          dataset = load_dataset("race", "all", split="test")
      elif task_name == "drop":
          dataset = load_dataset("drop", split="validation")
      elif task_name == "coqa":
          dataset = load_dataset("coqa", split="validation")
      elif task_name == "piqa":
          dataset = load_dataset("piqa", split="test")
      else:
          print("Invalid task name: {}".format(task_name), flush=True)
          return
  
      # read the dataset and add to ngrams
      for line in dataset:
          try:
              if task_name in [
                  "squad",
                  "triviaqa",
                  "webqa",
                  "race",
                  "drop",
              ]:
                  text = line["question"]
                  compute_ngrams_insert_dict(args, text, ngrams)
              elif task_name == "natural_questions":
                  text = line["question"]["text"]
                  compute_ngrams_insert_dict(args, text, ngrams)
              elif task_name == "coqa":
                  all_questions = line["questions"]
                  for question in all_questions:
                      compute_ngrams_insert_dict(args, question, ngrams)
              elif task_name == "piqa":
                  text = line["goal"]
                  compute_ngrams_insert_dict(args, text, ngrams)
          except Exception as e:
              print("Error:", e)
  
      print(
          " After task {} entities in ngrams {}, added {}".format(
              task_name, len(ngrams), len(ngrams) - entities_in_ngrams
          ),
          flush=True,
      )
  
imports: import argparse
  from functools import partial
  import json
  import multiprocessing
  import nltk
  import pickle
  import re
  import string
  import sys
  import time
label_desc: Use image node_id 8 for calling a global function with example usage: process_task(args, task_name, ngrams) without return types