code: def test_description_dict():
      seed = 42
      num_examples = 1
      task_names = ["hellaswag", "winogrande"]
      description_dict = {
          "hellaswag": "Label for the relevant action:\nSentences describing context, with an incomplete sentence trailing answer that plausibly completes the situation.",
          "winogrande": "Winograd schema sentence including a either a ___ blank with a missing word, making the pronoun ambiguous, or the same with the word filled in.",
      }
  
      task_dict = lm_eval.tasks.get_task_dict(task_names)
      for task_name, task in task_dict.items():
          rnd = random.Random()
          rnd.seed(seed)
  
          if task.has_training_docs():
              docs = task.training_docs()
          elif set == "val" and task.has_validation_docs():
              docs = task.validation_docs()
          elif set == "test" and task.has_test_docs():
              docs = task.test_docs()
  
          description = (
              description_dict[task_name]
              if description_dict and task_name in description_dict
              else ""
          )
  
          for _, doc in (
              zip(range(num_examples), docs)
              if num_examples > 0
              else enumerate(docs)
          ):
              ctx = task.fewshot_context(
                  doc=doc,
                  num_fewshot=1,
                  rnd=rnd,
                  description=description,
              )
              assert description in ctx
  
imports: import random
  import lm_eval.tasks
  import lm_eval.models
label_desc: Use image node_id 1 for calling a global function with example usage: test_description_dict() without return types