code: def testEmbeddingsNotTrainable(self):
      # NOTE: EMA embeddings are updated during the forward pass and not as part
      # of the optimizer step.
      model = vqvae.VectorQuantizerEMA(
          embedding_dim=6,
          num_embeddings=13,
          commitment_cost=0.5,
          decay=0.1,
      )
      self.assertFalse(model.embeddings.trainable)
  
file_path: sonnet/sonnet/src/nets/vqvae_test.py
imports: from absl.testing import parameterized
  import numpy
  from sonnet.src import test_utils
  from sonnet.src.nets import vqvae
  import tensorflow
  import tree
label_desc: Use image node_id 5 for calling the VqvaeTest obj's underlying member method code with example usage: obj.testEmbeddingsNotTrainable() without return types