code: def synthetic_item_response_theory():
      """One-parameter logistic item-response theory (IRT) model.
  
      This uses a dataset sampled from the prior. This dataset is a simulation of
      400 students each answering a subset of 100 unique questions, with a total of
      30012 questions answered.
  
      Returns:
        target: StanModel.
      """
      dataset = data.synthetic_item_response_theory()
      del dataset["test_student_ids"]
      del dataset["test_question_ids"]
      del dataset["test_correct"]
      return item_response_theory.item_response_theory(**dataset)
  
imports: from inference_gym.internal import data
  from inference_gym.tools.stan import brownian_motion
  from inference_gym.tools.stan import eight_schools
  from inference_gym.tools.stan import item_response_theory
  from inference_gym.tools.stan import log_gaussian_cox_process
  from inference_gym.tools.stan import logistic_regression
  from inference_gym.tools.stan import lorenz_system
  from inference_gym.tools.stan import probit_regression
  from inference_gym.tools.stan import radon_contextual_effects
  from inference_gym.tools.stan import radon_contextual_effects_halfnormal
  from inference_gym.tools.stan import sparse_logistic_regression
  from inference_gym.tools.stan import stochastic_volatility
label_desc: Use image node_id 17 for calling a global function with example usage: synthetic_item_response_theory() and returns: item_response_theory