code: def create_standard_hparams():
      return tf.contrib.training.HParams(
          # Data
          src="",
          tgt="",
          train_prefix="",
          dev_prefix="",
          test_prefix="",
          vocab_prefix="",
          embed_prefix="",
          out_dir="",
          # Networks
          num_units=512,
          num_encoder_layers=2,
          num_decoder_layers=2,
          dropout=0.2,
          unit_type="lstm",
          encoder_type="bi",
          residual=False,
          time_major=True,
          num_embeddings_partitions=0,
          num_enc_emb_partitions=0,
          num_dec_emb_partitions=0,
          # Attention mechanisms
          attention="scaled_luong",
          attention_architecture="standard",
          output_attention=True,
          pass_hidden_state=True,
          # Train
          optimizer="sgd",
          batch_size=128,
          init_op="uniform",
          init_weight=0.1,
          max_gradient_norm=5.0,
          learning_rate=1.0,
          warmup_steps=0,
          warmup_scheme="t2t",
          decay_scheme="luong234",
          colocate_gradients_with_ops=True,
          num_train_steps=12000,
          num_sampled_softmax=0,
          # Data constraints
          num_buckets=5,
          max_train=0,
          src_max_len=50,
          tgt_max_len=50,
          src_max_len_infer=0,
          tgt_max_len_infer=0,
          # Data format
          sos="<s>",
          eos="</s>",
          subword_option="",
          use_char_encode=False,
          check_special_token=True,
          # Misc
          forget_bias=1.0,
          num_gpus=1,
          epoch_step=0,  # record where we were within an epoch.
          steps_per_stats=100,
          steps_per_external_eval=0,
          share_vocab=False,
          metrics=["bleu"],
          log_device_placement=False,
          random_seed=None,
          # only enable beam search during inference when beam_width > 0.
          beam_width=0,
          length_penalty_weight=0.0,
          override_loaded_hparams=True,
          num_keep_ckpts=5,
          avg_ckpts=False,
          # For inference
          inference_indices=None,
          infer_batch_size=32,
          sampling_temperature=0.0,
          num_translations_per_input=1,
          infer_mode="greedy",
          # Language model
          language_model=False,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: create_standard_hparams() and returns: tf