code: def __init__(self, mean_module):
      train_x = torch.randn(10, 3)
      train_y = torch.randn(10)
      likelihood = gpytorch.likelihoods.GaussianLikelihood()
      super().__init__(train_x, train_y, likelihood)
      self.mean_module = mean_module
  
imports: import math
  import pickle
  import unittest
  import warnings
  from collections import OrderedDict
  import torch
  import gpytorch
  from gpytorch.constraints import GreaterThan
  from gpytorch.means import ConstantMean
  from gpytorch.priors import NormalPrior
  from gpytorch.test.base_mean_test_case import BaseMeanTestCase
  from gpytorch.utils.warnings import OldVersionWarning
label_desc: Use image node_id 1 to create a new _GPModel object from inherited base classes: gpytorch.models with example: obj = _GPModel(mean_module)