class_docstr: Synthetic plasma spectroscopy model.
  
  This uses is a synthetic dataset sampled from the model prior. This
  parameterization produces discontinuous log-probabilities, which makes it
  challenging for typical gradient-based inference methods.
class_docstr_tok: ["Synthetic","plasma","spectroscopy","model",".","This","uses","is","a","synthetic","dataset","sampled","from","the","model","prior",".","This","parameterization","produces","discontinuous","log-probabilities",",","which","makes","it","challenging","for","typical","gradient-based","inference","methods","."]
code: def __init__(self):
      dataset = data.synthetic_plasma_spectroscopy()
      super().__init__(
          name="synthetic_plasma_spectroscopy",
          pretty_name="Synthetic Plasma Spectroscopy",
          **dataset,
      )
  
imports: import numpy
  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
label_desc: Use image node_id 1 to create a new SyntheticPlasmaSpectroscopy object from inherited base classes: PlasmaSpectroscopy with example: obj = SyntheticPlasmaSpectroscopy()