code: def test_multivariate_rv_transform(shift, scale):
      mu = np.array([0, 0.9, -2.1])
      cov = np.array([[1, 0, 0.9], [0, 1, 0], [0.9, 0, 1]])
      x_rv_raw = pt.random.multivariate_normal(mu, cov=cov)
      x_rv = shift + x_rv_raw * scale
      x_rv.name = "x"
  
      x_vv = x_rv.clone()
      logp = conditional_logp({x_rv: x_vv})[x_vv]
      assert_no_rvs(logp)
  
      x_vv_test = np.array([5.0, 4.9, -6.3])
      scale_mat = scale * np.eye(x_vv_test.shape[0])
      np.testing.assert_allclose(
          logp.eval({x_vv: x_vv_test}),
          sp.stats.multivariate_normal.logpdf(
              x_vv_test,
              shift + mu * scale,
              scale_mat @ cov @ scale_mat.T,
          ),
      )
  
imports: import numpy
  import pytensor
  import pytensor.tensor
  import pytest
  import scipy
  import scipy.special
  from pytensor.graph.basic import equal_computations
  from pymc.distributions.continuous import Cauchy, ChiSquared
  from pymc.logprob.basic import conditional_logp, icdf, logcdf, logp
  from pymc.logprob.transforms import ArccoshTransform, ArcsinhTransform, ArctanhTransform, ChainedTransform, CoshTransform, ErfcTransform, ErfcxTransform, ErfTransform, ExpTransform, LocTransform, LogTransform, ScaleTransform, SinhTransform, TanhTransform, Transform
  from pymc.logprob.utils import ParameterValueError
  from pymc.testing import Rplusbig, Vector, assert_no_rvs
  from tests.distributions.test_transform import check_jacobian_det
label_desc: Use image node_id 10 for calling a global function with example usage: test_multivariate_rv_transform(shift, scale) without return types