code: def _data(
      _f: Callable[[tf.Tensor], tf.Tensor], _f_minimum: tf.Tensor
  ) -> Tuple[tf.Variable, tf.Variable]:
      n_initial_data = 3
      n_outputs, n_features = _f_minimum.shape
  
      rng = np.random.default_rng(20220126)
      X = tf.Variable(
          rng.random((n_initial_data, n_features)),
          shape=[None, n_features],
          dtype=default_float(),
          trainable=False,
      )
      Y = tf.Variable(
          _f(X),
          shape=[None, n_outputs],
          dtype=default_float(),
          trainable=False,
      )
  
      return X, Y
  
imports: from typing import Any, Callable, DefaultDict, Generic, Iterator, List, Set, Tuple, Type, TypeVar, cast
  import numpy
  import pytest
  import tensorflow
  from _pytest.fixtures import SubRequest
  from check_shapes import check_shapes
  import gpflow
  from gpflow.base import RegressionData
  from gpflow.config import default_float
  from gpflow.inducing_variables import InducingPoints, InducingVariables
  from gpflow.kernels import Kernel, Matern52
  from gpflow.likelihoods import Exponential, Likelihood
  from gpflow.models import GPR, SGPR, SVGP, VGP, GPModel, training_loss_closure
  from gpflow.models.vgp import update_vgp_data
  from gpflow.posteriors import AbstractPosterior, PrecomputeCacheType
label_desc: Use image node_id 18 for calling a global function with example usage: _data(_f, _f_minimum) and returns: X,  Y