code: def get_model(
      self,
      Z: InducingPointsLike,
      num_latent_gps: int,
      data: Optional[gpflow.base.RegressionData] = None,
  ) -> gpflow.models.GPModel:
      params: Dict[str, Any] = dict(
          kernel=self.kernel, num_latent_gps=num_latent_gps
      )
  
      if self.whiten is not None and self.q_diag is not None:
          params.update(
              inducing_variable=Z,
              whiten=self.whiten,
              q_diag=self.q_diag,
          )
  
      if self.requires_inducing_variables:
          params.update(dict(inducing_variable=Z))
  
      if self.requires_data:
          params.update(dict(data=data))
  
      if self.requires_likelihood:
          params.update(dict(likelihood=self.likelihood))
  
      return self.model_class(**params)
  
file_path: GPflow/tests/gpflow/models/test_model_predict.py
imports: from typing import Any, Dict, Optional, Type
  import numpy
  import pytest
  import gpflow
  from gpflow.inducing_variables import InducingPoints
  from gpflow.kernels import Kernel, Matern32
  from gpflow.models.util import InducingPointsLike
label_desc: Use image node_id 2 for calling the ModelSetup obj's underlying member method code with example usage: obj.get_model(Z, num_latent_gps, data) and returns: self