code: def _seq2seq_lstm_harness(name, **kwargs):
      model = seq2seq_lstm.Seq2seq(
          teacher_force=True, hidden_size=2, vocab_size=4
      )
      encoder_inputs = np.zeros(
          (1, 2, 4), np.float32
      )  # [batch, inp_len, vocab]
      decoder_inputs = np.zeros(
          (1, 3, 4), np.float32
      )  # [batch, outp_len, vocab]
      rngs = {
          "params": random.PRNGKey(0),
          "lstm": random.PRNGKey(1),
      }
      xs = [encoder_inputs, decoder_inputs]
      variables = model.init(rngs, *xs)
      apply = functools.partial(
          model.apply, rngs={"lstm": random.PRNGKey(2)}
      )
      return ModelHarness(name, apply, variables, xs, **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 9 for calling a global function with example usage: _seq2seq_lstm_harness(name) and returns: ModelHarness