code: def __init__(
      self,
      input_dims,
      output_dims,
      num_inducing=300,
      inducing_points=None,
      mean_type="constant",
      Q=8,
  ):
      if output_dims is None:
          # An output_dims of None implies there is only one GP in this layer
          # (e.g., the last layer for univariate regression).
          inducing_points = torch.randn(num_inducing, input_dims)
      else:
          inducing_points = torch.randn(
              output_dims, num_inducing, input_dims
          )
  
      # Let's use mean field / diagonal covariance structure.
      variational_distribution = MeanFieldVariationalDistribution(
          num_inducing_points=num_inducing,
          batch_shape=torch.Size([output_dims])
          if output_dims is not None
          else torch.Size([]),
      )
  
      # Standard variational inference.
      variational_strategy = VariationalStrategy(
          self,
          inducing_points,
          variational_distribution,
          learn_inducing_locations=True,
      )
  
      batch_shape = (
          torch.Size([])
          if output_dims is None
          else torch.Size([output_dims])
      )
      super().__init__(variational_strategy, input_dims, output_dims, Q)
  
      if mean_type == "constant":
          self.mean_module = ConstantMean(batch_shape=batch_shape)
      elif mean_type == "linear":
          self.mean_module = LinearMean(
              input_dims, batch_shape=batch_shape
          )
  
      self.covar_module = ScaleKernel(
          MaternKernel(
              batch_shape=batch_shape, ard_num_dims=input_dims
          ),
          batch_shape=batch_shape,
          ard_num_dims=None,
      )
  
imports: import unittest
  import torch
  import gpytorch
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean, LinearMean
  from gpytorch.models.deep_gps.dspp import DSPP, DSPPLayer
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.variational import MeanFieldVariationalDistribution, VariationalStrategy
label_desc: Use image node_id 1 to create a new DSPPHiddenLayer object from inherited base classes: DSPPLayer with example: obj = DSPPHiddenLayer(input_dims, output_dims, num_inducing, inducing_points, mean_type, Q)