code: def forward(self, x):
      self.last_fantasize_flag = fantasize.on()
      if self.training:
          x = self.transform_inputs(x)
      self.transformed_call_args.append(x)
      mean_x = self.mean_module(x)
      covar_x = self.covar_module(x)
      return MultivariateNormal(mean_x, covar_x)
  
imports: from __future__ import annotations
  import math
  from typing import Optional, Tuple
  import torch
  from botorch.acquisition.objective import PosteriorTransform
  from botorch.models.gpytorch import GPyTorchModel
  from botorch.models.model import FantasizeMixin, Model
  from botorch.models.transforms.outcome import Standardize
  from botorch.models.utils import add_output_dim
  from botorch.models.utils.assorted import fantasize
  from botorch.posteriors.posterior import Posterior
  from botorch.utils.datasets import MultiTaskDataset, SupervisedDataset
  from gpytorch.distributions.multivariate_normal import MultivariateNormal
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood, GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.models.exact_gp import ExactGP
  from torch import Size, Tensor
  from torch.nn.functional import pad
label_desc: Use image node_id 2 for calling the SimpleGPyTorchModel obj's underlying member method code with example usage: obj.forward(x) and returns: MultivariateNormal