code: def predict_all(
      models: Sequence[SVGP],
      Xnew: tf.Tensor,
      full_cov: bool,
      full_output_cov: bool,
  ) -> Tuple[List[tf.Tensor], List[tf.Tensor]]:
      """
      Returns the mean and variance of f(Xnew) for each model in `models`.
      """
      ms, vs = [], []
      for model in models:
          m, v = model.predict_f(
              Xnew, full_cov=full_cov, full_output_cov=full_output_cov
          )
          ms.append(m)
          vs.append(v)
      return ms, vs
  
imports: from typing import Callable, List, Sequence, Tuple, cast
  import numpy
  import pytest
  import scipy
  import tensorflow
  from _pytest.fixtures import SubRequest
  from check_shapes import ShapeChecker, check_shapes
  import gpflow
  import gpflow.inducing_variables.multioutput
  import gpflow.kernels.multioutput
  from gpflow import set_trainable
  from gpflow.base import AnyNDArray, RegressionData
  from gpflow.conditionals import sample_conditional
  from gpflow.conditionals.util import fully_correlated_conditional, fully_correlated_conditional_repeat, independent_interdomain_conditional, sample_mvn
  from gpflow.config import default_float, default_jitter
  from gpflow.inducing_variables import InducingPoints
  from gpflow.kernels import SquaredExponential
  from gpflow.likelihoods import Gaussian
  from gpflow.models import SVGP
label_desc: Use image node_id 1 for calling a global function with example usage: predict_all(models, Xnew, full_cov, full_output_cov) and returns: ms,  vs