code: def test_other_models_full_cov(
      model_setup: ModelSetup,
      input_dim: int,
      output_dim: int,
      N: int,
      Ntest: int,
      M: int,
  ) -> None:
      covar_shape = (output_dim, Ntest, Ntest)
      X, Y = rng.randn(N, input_dim), rng.randn(N, output_dim)
      Z = InducingPoints(rng.randn(M, input_dim))
      Xtest = rng.randn(Ntest, input_dim)
      model_gp = model_setup.get_model(
          Z, num_latent_gps=output_dim, data=(X, Y)
      )
  
      mu1, var = model_gp.predict_f(Xtest, full_cov=False)
      mu2, covar = model_gp.predict_f(Xtest, full_cov=True)
  
      assert np.allclose(mu1, mu2, atol=1.0e-10)
      assert covar.shape == covar_shape
      assert var.shape == (Ntest, output_dim)
      for i in range(output_dim):
          assert np.allclose(var[:, i], np.diag(covar[i, :, :]))
  
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 5 for calling a global function with example usage: test_other_models_full_cov(model_setup, input_dim, output_dim, N, Ntest, M) without return types