code: def __init__(self):
      # Let's use a different set of inducing points for each latent function
      inducing_points = torch.rand(3, 10, 1)
  
      # We have to mark the CholeskyVariationalDistribution as batch
      # so that we learn a variational distribution for each task
      variational_distribution = (
          gpytorch.variational.CholeskyVariationalDistribution(
              inducing_points.size(-2), batch_shape=torch.Size([3])
          )
      )
  
      # We have to wrap the VariationalStrategy in a LMCVariationalStrategy
      # so that the output will be a MultitaskMultivariateNormal rather than a batch output
      variational_strategy = (
          gpytorch.variational.LMCVariationalStrategy(
              gpytorch.variational.VariationalStrategy(
                  self,
                  inducing_points,
                  variational_distribution,
                  learn_inducing_locations=True,
              ),
              num_tasks=4,
              num_latents=3,
              latent_dim=-1,
          )
      )
  
      super().__init__(variational_strategy)
  
      # The mean and covariance modules should be marked as batch
      # so we learn a different set of hyperparameters
      self.mean_module = gpytorch.means.ConstantMean(
          batch_shape=torch.Size([3])
      )
      self.covar_module = gpytorch.kernels.ScaleKernel(
          gpytorch.kernels.RBFKernel(batch_shape=torch.Size([3])),
          batch_shape=torch.Size([3]),
      )
  
file_path: gpytorch/test/examples/test_lmc_svgp_regression.py
imports: import math
  import os
  import random
  import unittest
  import torch
  import gpytorch
  from gpytorch.likelihoods import GaussianLikelihood, MultitaskGaussianLikelihood
label_desc: Use image node_id 1 to create a new LMCModel object from inherited base classes: gpytorch.models with example: obj = LMCModel()