code: def synthetic_log_gaussian_cox_process():
      """Log-Gaussian Cox Process model.
  
      This dataset was simulated by constructing a 10 by 10 grid of equidistant 2D
      locations with spacing = 1, and then sampling from the prior to determine the
      counts at those locations.
  
      Returns:
        target: StanModel.
      """
      dataset = data.synthetic_log_gaussian_cox_process()
      return log_gaussian_cox_process.log_gaussian_cox_process(
          **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 18 for calling a global function with example usage: synthetic_log_gaussian_cox_process() and returns: log_gaussian_cox_process