code: def initialize_cache(inputs, max_decode_len, config):
      """Initialize a cache for a given input shape and max decode length."""
      target_shape = (inputs.shape[0], max_decode_len) + inputs.shape[
          2:
      ]
      initial_variables = models.Transformer(config).init(
          jax.random.key(0),
          jnp.ones(inputs.shape, config.dtype),
          jnp.ones(target_shape, config.dtype),
      )
      return initial_variables["cache"]
  
imports: import collections
  import functools
  import os
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import jax_utils
  from flax import linen
  from flax.training import checkpoints
  from flax.training import common_utils
  from flax.training import dynamic_scale
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import bleu
  import decode
  import input_pipeline
  import models
label_desc: Use image node_id 8 for calling a global function with example usage: initialize_cache(inputs, max_decode_len, config) and returns: initial_variables