class_docstr: An implementation of the predictive log likelihood extended to DSPPs as discussed in Jankowiak et al., 2020.
  
  If you are using a DSPP model, this is the loss object you want to create and optimize over.
  
  This loss object is compatible only with models of type :obj:~gpytorch.models.deep_gps.DSPP
class_docstr_tok: ["An","implementation","of","the","predictive","log","likelihood","extended","to","DSPPs","as","discussed","in","Jankowiak","et","al.",",","2020",".","If","you","are","using","a","DSPP","model",",","this","is","the","loss","object","you","want","to","create","and","optimize","over",".","This","loss","object","is","compatible","only","with","models","of","type",":","obj",":","~gpytorch.models.deep_gps.DSPP"]
code: def __init__(
      self, likelihood, model, num_data, beta=1.0, combine_terms=True
  ):
      if not combine_terms:
          raise ValueError(
              "The base marginal log likelihood object should combine terms "
              "when used in conjunction with a DeepApproximateMLL."
          )
  
      if not isinstance(model, DSPP):
          raise ValueError(
              "DeepPredictiveLogLikelihood can only be used with a DSPP model."
          )
  
      super().__init__(likelihood, model, num_data, beta, combine_terms)
  
imports: from ..models.deep_gps.dspp import DSPP
  from ._approximate_mll import _ApproximateMarginalLogLikelihood
label_desc: Use image node_id 1 to create a new DeepPredictiveLogLikelihood object from inherited base classes: _ApproximateMarginalLogLikelihood with example: obj = DeepPredictiveLogLikelihood(likelihood, model, num_data, beta, combine_terms)