code: def _transformer_lm1b_harness(name, **kwargs):
      config = lm1b.TransformerConfig(**_full_transformer_kwargs())
      model = lm1b.TransformerLM(config=config)
      x = np.zeros((2, 1), np.float32)
      rng1, rng2 = random.split(random.PRNGKey(0))
      variables = model.init(rng1, x)
  
      def apply(*args):
          # Don't return the new state (containing the cache).
          output, _ = model.apply(
              *args, rngs={"cache": rng2}, mutable=["cache"]
          )
          return output
  
      return ModelHarness(name, apply, variables, [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 12 for calling a global function with example usage: _transformer_lm1b_harness(name) and returns: ModelHarness, output