class_docstr: A wrapper to make a GPyTorch approximate marginal log likelihoods compatible with Deep GPs.
  
  Example:
      >>> deep_mll = gpytorch.mlls.DeepApproximateMLL(
      >>>     gpytorch.mlls.VariationalELBO(likelihood, model, num_data=1000)
      >>> )
  
  :param ~gpytorch.mlls._ApproximateMarginalLogLikelihood base_mll: The base
      approximate MLL
class_docstr_tok: ["A","wrapper","to","make","a","GPyTorch","approximate","marginal","log","likelihoods","compatible","with","Deep","GPs",".","Example",":",">",">",">","deep_mll","=","gpytorch.mlls.DeepApproximateMLL","(",">",">",">","gpytorch.mlls.VariationalELBO","(","likelihood",",","model",",","num_data=1000",")",">",">",">",")",":","param","~gpytorch.mlls._ApproximateMarginalLogLikelihood","base_mll",":","The","base","approximate","MLL"]
code: def __init__(self, base_mll):
      if not base_mll.combine_terms:
          raise ValueError(
              "The base marginal log likelihood object should combine terms "
              "when used in conjunction with a DeepApproximateMLL."
          )
      super().__init__(
          base_mll.likelihood,
          base_mll.model,
          num_data=base_mll.num_data,
          beta=base_mll.beta,
      )
      self.base_mll = base_mll
  
imports: from ._approximate_mll import _ApproximateMarginalLogLikelihood
label_desc: Use image node_id 1 to create a new DeepApproximateMLL object from inherited base classes: _ApproximateMarginalLogLikelihood with example: obj = DeepApproximateMLL(base_mll)