code: def model() -> SVGP:
      return SVGP(
          kernel=gpflow.kernels.SquaredExponential(
              lengthscales=Data.ls, variance=Data.var
          ),
          likelihood=gpflow.likelihoods.Gaussian(),
          inducing_variable=Data.Z,
          q_diag=True,
      )
  
file_path: GPflow/tests/gpflow/test_base_training.py
imports: from typing import Any, Mapping
  import numpy
  import pytest
  import tensorflow
  import gpflow
  from gpflow.models import SVGP
  from gpflow.utilities import leaf_components, multiple_assign, read_values
label_desc: Use image node_id 1 for calling a global function with example usage: model() and returns: SVGP