code: def forward(self, x, mean_input=None, **kwargs):
      mean_x = self.mean_module(x)
      covar_x = self.covar_module(x)
      return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
  
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 2 for calling the DSPPHiddenLayer obj's underlying member method code with example usage: obj.forward(x, mean_input) and returns: gpytorch