code: def create_q(
      inducing_variable: InducingVariables,
      *,
      row_scale: int = 1,
      column_scale: int = 1,
  ) -> Tuple[bool, tf.Tensor, tf.Tensor]:
      n_inducing_points = inducing_variable.num_inducing
      rng = np.random.default_rng(20220133)
      q_diag = True
      q_mu = tf.constant(
          rng.random((row_scale * n_inducing_points, column_scale))
      )
      q_sqrt = (
          tf.constant(
              rng.random((row_scale * n_inducing_points, column_scale))
          )
          ** 2
      )
      return q_diag, q_mu, q_sqrt
  
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 6 for calling a global function with example usage: create_q(inducing_variable) and returns: q_diag,  q_mu,  q_sqrt