code: def imagetransformer_latent_tiny():
      """Tiny set of hparams for a latent image model."""
      hparams = transformer.transformer_small()
      hparams.batch_size = 2
      hparams.num_hidden_layers = 3
      hparams.hidden_size = 16
      hparams.filter_size = 32
      hparams.compress_filter_size = 64
      hparams.ffn_layer = "conv_hidden_relu"
      hparams.layer_prepostprocess_dropout = 0.2
      hparams.layer_preprocess_sequence = "none"
      hparams.layer_postprocess_sequence = "dan"
      hparams.dropout = 0.3
      hparams.pos = "timing"
      hparams.num_encoder_layers = 1
      hparams.num_decoder_layers = 2
      hparams.use_pad_remover = False
      hparams.add_hparam("logit_normalization", True)
      hparams.add_hparam("bottleneck_kind", "dvq")
      hparams.add_hparam("bottleneck_bits", 4)
      hparams.add_hparam("num_residuals", 1)
      hparams.add_hparam("use_gold_targets", False)
      hparams.add_hparam("do_compress_attend", False)
      hparams.add_hparam("do_decompress_attend", False)
      hparams.add_hparam("drop_inputs", False)
      hparams.add_hparam("num_compress_steps", 2)
      hparams.add_hparam("startup_steps", 10000)
      hparams.add_hparam("mask_startup_steps", 50000)
      hparams.add_hparam("latent_dropout", 0.0)
      hparams.add_hparam("decode_autoregressive", False)
      hparams.add_hparam("vq_beta", 0.25)
      hparams.add_hparam("vq_epsilon", 1e-5)
      hparams.add_hparam("vq_decay", 0.999)
      hparams.add_hparam("ema", False)
      hparams.add_hparam("soft_em", True)
      hparams.add_hparam("num_samples", 1)
      hparams.add_hparam("num_latent_layers", 2)
      hparams.add_hparam("num_res_layers", 2)
      hparams.add_hparam("res_kernel_size", 3)
      hparams.add_hparam("num_blocks", 1)
      hparams.add_hparam("reshape_method", "slice")
      hparams.add_hparam("shared_rel", False)
      hparams.add_hparam("block_size", 1)
      hparams.add_hparam("kernel_size", 3)
      hparams.add_hparam("img_len", 8)
      hparams.add_hparam("num_channels", 1)
      hparams.add_hparam("local_and_global_att", False)
      hparams.add_hparam("block_length", 32)
      hparams.add_hparam("block_width", 128)
      hparams.add_hparam(
          "dec_attention_type", cia.AttentionType.LOCAL_1D
      )
      hparams.add_hparam(
          "latent_attention_type", cia.AttentionType.GLOBAL
      )
      hparams.add_hparam("block_raster_scan", False)
      hparams.add_hparam("num_latents", 1)
      hparams.add_hparam("q_filter_width", 1)
      hparams.add_hparam("kv_filter_width", 1)
      return hparams
  
file_path: tensor2tensor/tensor2tensor/layers/latent_layers_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  from tensor2tensor.layers import common_image_attention
  from tensor2tensor.layers import discretization
  from tensor2tensor.layers import latent_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import test_utils
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 for calling a global function with example usage: imagetransformer_latent_tiny() and returns: hparams