code: def _transformer_wmt_harness(name, **kwargs):
      config = wmt.TransformerConfig(**_full_transformer_kwargs())
      model = wmt.Transformer(config=config)
      x = np.zeros((2, 1), np.float32)
      variables = model.init(random.PRNGKey(0), x, x)
  
      def apply(*args):
          # Don't return the new state (containing the cache).
          output, _ = model.apply(*args, mutable=["cache"])
          return output
  
      return ModelHarness(name, apply, variables, [x, x], **kwargs)
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import dataclasses
  import functools
  from typing import Any, Callable, Optional, Union
  import re
  import numpy
  import jraph
  from jax.experimental.jax2tf.tests.flax_models import actor_critic
  from jax.experimental.jax2tf.tests.flax_models import bilstm_classifier
  from jax.experimental.jax2tf.tests.flax_models import cnn
  from jax.experimental.jax2tf.tests.flax_models import gnn
  from jax.experimental.jax2tf.tests.flax_models import resnet
  from jax.experimental.jax2tf.tests.flax_models import seq2seq_lstm
  from jax.experimental.jax2tf.tests.flax_models import transformer_lm1b
  from jax.experimental.jax2tf.tests.flax_models import transformer_nlp_seq
  from jax.experimental.jax2tf.tests.flax_models import transformer_wmt
  from jax.experimental.jax2tf.tests.flax_models import vae
  import jax
  from jax import random
  import tensorflow
label_desc: Use image node_id 14 for calling a global function with example usage: _transformer_wmt_harness(name) and returns: ModelHarness, output