code: def __init__(self, train_x_shape, num_inducing, hidden_dim=3, Q=3):
      hidden_layer1 = DSPPHiddenLayer(
          input_dims=train_x_shape[-1],
          output_dims=hidden_dim,
          num_inducing=num_inducing,
          mean_type="linear",
          Q=Q,
      )
      hidden_layer2 = DSPPHiddenLayer(
          input_dims=hidden_layer1.output_dims,
          output_dims=hidden_dim,
          mean_type="linear",
          num_inducing=num_inducing,
          Q=Q,
      )
      last_layer = DSPPHiddenLayer(
          input_dims=hidden_layer2.output_dims,
          output_dims=None,
          mean_type="constant",
          num_inducing=num_inducing,
          Q=Q,
      )
  
      likelihood = GaussianLikelihood()
  
      super().__init__(Q)
      self.likelihood = likelihood
      self.last_layer = last_layer
      self.hidden_layer2 = hidden_layer2
      self.hidden_layer1 = hidden_layer1
  
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 ThreeLayerDSPP object from inherited base classes: DSPP with example: obj = ThreeLayerDSPP(train_x_shape, num_inducing, hidden_dim, Q)