code: def imagetransformer_ae_imagenet():
      """For 64x64 ImageNet. ~56M trainable variables."""
      hparams = imagetransformer_ae_cifar()
      hparams.max_length = int(64 * 64 * 3)
      hparams.img_len = 64
      hparams.num_heads = 4  # Heads are expensive on TPUs.
      # Reduce architecture from 32x32 CIFAR-10 in order to fit in memory.
      hparams.num_decoder_layers = 8
      hparams.num_compress_steps = 2
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import math
  import os
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_image_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import discretization
  from tensor2tensor.layers import latent_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import beam_search
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import expert_utils
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 16 for calling a global function with example usage: imagetransformer_ae_imagenet() and returns: hparams