code: def vqa_attention_base():
      """VQA attention baseline hparams."""
      hparams = common_hparams.basic_params1()
      hparams.batch_size = 128
      hparams.use_fixed_batch_size = (True,)
      hparams.optimizer = "adam"
      hparams.optimizer_adam_beta1 = 0.9
      hparams.optimizer_adam_beta2 = 0.999
      hparams.optimizer_adam_epsilon = 1e-8
      hparams.weight_decay = 0.0
      hparams.clip_grad_norm = 0.0
      hparams.initializer = "xavier"
      hparams.learning_rate = 0.5
      hparams.learning_rate_schedule = "legacy"
      hparams.learning_rate_warmup_steps = 0
      hparams.learning_rate_decay_scheme = "exp"
      hparams.learning_rate_decay_rate = 0.5
      hparams.learning_rate_decay_steps = 50000
      hparams.dropout = 0.5
      hparams.summarize_grads = True
      hparams.summarize_vars = True
  
      # not used hparams
      hparams.label_smoothing = 0.0
      hparams.multiply_embedding_mode = ""
  
      # add new hparams
      # preprocess
      hparams.add_hparam("resize_side", 512)
      hparams.add_hparam("height", 448)
      hparams.add_hparam("width", 448)
      hparams.add_hparam("distort", True)
  
      hparams.add_hparam("train_resnet", False)
      hparams.add_hparam("rnn_type", "lstm")
      hparams.add_hparam("num_rnn_layers", 1)
      hparams.add_hparam("max_question_length", 15)
      # lstm hidden size
      hparams.hidden_size = 512
  
      hparams.add_hparam("attn_dim", 512)
      hparams.add_hparam("num_glimps", 2)
  
      hparams.add_hparam("num_mlp_layers", 1)
      hparams.add_hparam("mlp_dim", 1024)
  
      hparams.add_hparam("image_input_type", "image")
      hparams.add_hparam("image_model_fn", "resnet_v1_152")
      hparams.add_hparam("image_feat_size", 0)
  
      # self attention parts
      hparams.norm_type = "layer"
      hparams.layer_preprocess_sequence = "n"
      hparams.layer_postprocess_sequence = "da"
      hparams.layer_prepostprocess_dropout = 0.3
      hparams.attention_dropout = 0.1
      hparams.relu_dropout = 0.1
      hparams.image_hidden_size = 2048
      hparams.add_hparam("num_encoder_layers", 1)
      # Attention-related flags.
      hparams.add_hparam("num_heads", 8)
      hparams.add_hparam("attention_key_channels", 0)
      hparams.add_hparam("attention_value_channels", 0)
      hparams.add_hparam("image_filter_size", 1024)
      hparams.add_hparam("self_attention_type", "dot_product")
      hparams.add_hparam("scale_dotproduct", True)
  
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import vqa_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  from tensorflow.contrib import rnn
  from tensorflow.contrib.layers.python.layers import utils
  from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_152
  from tensorflow.contrib.slim.python.slim.nets.resnet_v2 import resnet_v2_152
label_desc: Use image node_id 6 for calling a global function with example usage: vqa_attention_base() and returns: hparams