code: def strategy_cls(
      model,
      inducing_points,
      variational_distribution,
      learn_inducing_locations,
  ):
      base_inducing_points = torch.randn(
          8, inducing_points.size(-1), device=inducing_points.device
      )
      base_variational_distribution = (
          gpytorch.variational.CholeskyVariationalDistribution(8)
      )
      return (
          gpytorch.variational.OrthogonallyDecoupledVariationalStrategy(
              gpytorch.variational.VariationalStrategy(
                  model,
                  base_inducing_points,
                  base_variational_distribution,
                  learn_inducing_locations,
              ),
              inducing_points,
              variational_distribution,
          )
      )
  
imports: import unittest
  import torch
  import gpytorch
  from gpytorch.test.variational_test_case import VariationalTestCase
label_desc: Use image node_id 2 for calling a global function with example usage: strategy_cls(model, inducing_points, variational_distribution, learn_inducing_locations) and returns: gpytorch