code: def testVarianceScalingError(self):
      rng = random.PRNGKey(0)
      shape = (5,)
      initializer = nn.initializers.variance_scaling(
          scale=1.0, mode="fan_avg", distribution="truncated_normal"
      )
  
      with self.assertRaisesRegex(
          ValueError,
          "Can't compute input and output sizes of a 1"
          "-dimensional weights tensor. Must be at least 2D.",
      ):
          initializer(rng, shape)
  
imports: import collections
  from functools import partial
  import itertools
  import unittest
  from absl.testing import absltest
  from absl.testing import parameterized
  import scipy.stats
  from jax._src import config
  from jax._src import core
  from jax._src import test_util
  from jax._src import ad_checkpoint
  from jax.test_util import check_grads
  from jax import nn
  from jax import random
  import jax
  import jax.numpy
label_desc: Use image node_id 5 for calling the NNInitializersTest obj's underlying member method code with example usage: obj.testVarianceScalingError() without return types