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 forward(self, approximate_dist_f, target, **kwargs):
      return self.base_mll.forward(
          approximate_dist_f, target, **kwargs
      ).mean(0)
  
imports: from ._approximate_mll import _ApproximateMarginalLogLikelihood
label_desc: Use image node_id 3 for calling the DeepApproximateMLL obj's underlying member method code with example usage: obj.forward(approximate_dist_f, target) and returns: self