code: def neural_assistant_tiny():
      """HParams for tiny neural_assistant model."""
      hparams = transformer.transformer_tiny_tpu()
      hparams.add_hparam(
          "pos_weight", 1.0
      )  # weight for positive triples
      hparams.add_hparam(
          "similarity_fuction", "bilinear"
      )  # dot_product or bilinear
      hparams.add_hparam(
          "pool_technique", "average"
      )  # avg or max pool or last
      hparams.add_hparam(
          "last_k", 1
      )  # number of last indices for averaging
      hparams.add_hparam(
          "max_triple_length", 30
      )  # max length of every triple
      hparams.add_hparam(
          "train_triple_num", 5000
      )  # max number of triples during training
      hparams.add_hparam(
          "attend_kb", True
      )  # if False, it's a transformer model
      hparams.add_hparam(
          "kb_loss_weight", 0.0
      )  # weight for distant supervision
      hparams.add_hparam("test_triple_num", 28483)  # max triples of KB
      hparams.add_hparam("margin", 1.0)  # KB training max-margin loss
      hparams.add_hparam(
          "num_negative_samples", 1
      )  # Sampling number of different adversarial training examples
      hparams.add_hparam("kb_train_weight", 0.0)
      # KB_training loss weight which combines Language model and KB selection loss
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import six
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 6 for calling a global function with example usage: neural_assistant_tiny() and returns: hparams