code: def _make_montecarlo_x_mu_var_y() -> Sequence[tf.Tensor]:
      x_mu_var_y = [tf.random.normal((3, 10), dtype=tf.float64)] * 4
      x_mu_var_y[2] = 0.01 * (x_mu_var_y[2] ** 2)
      return x_mu_var_y
  
imports: from typing import Sequence, Tuple
  import numpy
  import pytest
  import tensorflow
  from numpy.testing import assert_allclose
  import gpflow
  import gpflow.ci_utils
  from gpflow.base import TensorType
  from gpflow.config import default_float, default_int
  from gpflow.likelihoods import HeteroskedasticTFPConditional, Likelihood, MonteCarloLikelihood, MultiLatentLikelihood, MultiLatentTFPConditional, QuadratureLikelihood, ScalarLikelihood, Bernoulli, Beta, Exponential, Gamma, Gaussian, GaussianMC, MultiClass, Ordinal, Poisson, StudentT
label_desc: Use image node_id 9 for calling a global function with example usage: _make_montecarlo_x_mu_var_y() and returns: x_mu_var_y