class_docstr: 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.
class_docstr_tok: ["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","."]
code: def __init__(self):
      dataset = data.synthetic_log_gaussian_cox_process()
      super(SyntheticLogGaussianCoxProcess, self).__init__(
          name="synthetic_log_gaussian_cox_process",
          pretty_name="Synthetic Log-Gaussian Cox Process",
          **dataset,
      )
  
file_path: probability/spinoffs/inference_gym/inference_gym/targets/log_gaussian_cox_process.py
imports: import tensorflow.compat.v2
  import tensorflow_probability
  from inference_gym.internal import data
  from inference_gym.targets import bayesian_model
  from inference_gym.targets import model
  from inference_gym.targets.ground_truth import synthetic_log_gaussian_cox_process
label_desc: Use image node_id 1 to create a new SyntheticLogGaussianCoxProcess object from inherited base classes: LogGaussianCoxProcess with example: obj = SyntheticLogGaussianCoxProcess()