class_docstr: 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.
class_docstr_tok: ["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","."]
code: def __init__(self):
      dataset = data.synthetic_item_response_theory()
      del dataset["test_student_ids"]
      del dataset["test_question_ids"]
      del dataset["test_correct"]
      super(SyntheticItemResponseTheory, self).__init__(
          name="synthetic_item_response_theory",
          pretty_name="Synthetic Item-Response Theory",
          **dataset,
      )
  
file_path: probability/spinoffs/inference_gym/inference_gym/targets/item_response_theory.py
imports: import functools
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.python.internal import prefer_static
  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_item_response_theory
label_desc: Use image node_id 1 to create a new SyntheticItemResponseTheory object from inherited base classes: ItemResponseTheory with example: obj = SyntheticItemResponseTheory()