class_docstr: A container module to build a DSPP
  This module should contain :obj:`~gpytorch.models.deep_gps.DSPPLayer`
  modules, and can also contain other modules as well.
  
  This Module contains an additional set of parameters, `raw_quad_weights`, that represent the mixture weights for
  the output distribution.
class_docstr_tok: ["A","container","module","to","build","a","DSPP","This","module","should","contain",":","obj",":","`","~gpytorch.models.deep_gps.DSPPLayer","`","modules",",","and","can","also","contain","other","modules","as","well",".","This","Module","contains","an","additional","set","of","parameters",",","`","raw_quad_weights","`",",","that","represent","the","mixture","weights","for","the","output","distribution","."]
code: def __init__(self, num_quad_sites):
      super().__init__()
      self.num_quad_sites = num_quad_sites
      self.register_parameter(
          "raw_quad_weights",
          torch.nn.Parameter(torch.randn(self.num_quad_sites)),
      )
  
file_path: gpytorch/gpytorch/models/deep_gps/dspp.py
imports: import torch
  from linear_operator.operators import BlockDiagLinearOperator
  from ...None import settings
  from ...distributions import MultitaskMultivariateNormal
  from ..approximate_gp import ApproximateGP
  from .deep_gp import DeepGP, DeepGPLayer
label_desc: Use image node_id 1 to create a new DSPP object from inherited base classes: DeepGP with example: obj = DSPP(num_quad_sites)