code: def _make_hparams(
      train_input_pattern,
      eval_input_pattern,
      model_dir,
      list_size=5,
      num_train_steps=1,
      num_eval_steps=1,
      checkpoint_secs=1,
      num_checkpoints=2,
      listwise_inference=False,
      loss_key=None,
  ):
      return dict(
          train_input_pattern=train_input_pattern,
          eval_input_pattern=eval_input_pattern,
          learning_rate=0.01,
          train_batch_size=8,
          eval_batch_size=8,
          checkpoint_secs=checkpoint_secs,
          num_checkpoints=num_checkpoints,
          num_train_steps=num_train_steps,
          num_eval_steps=num_eval_steps,
          loss=loss_key,
          list_size=list_size,
          listwise_inference=listwise_inference,
          convert_labels_to_binary=False,
          model_dir=model_dir,
      )
  
imports: import os
  from absl.testing import parameterized
  import tensorflow
  from tensorflow import estimator
  import tensorflow_ranking
  from google.protobuf import text_format
  from tensorflow_ranking.extension import pipeline
  from tensorflow_serving.apis import input_pb2
label_desc: Use image node_id 6 for calling a global function with example usage: _make_hparams(train_input_pattern, eval_input_pattern, model_dir, list_size, num_train_steps, num_eval_steps, checkpoint_secs, num_checkpoints, listwise_inference, loss_key) and returns: dict