code: def default_hparams():
      """Generates the hparams used to train note rnn used in paper."""
      return contrib_training.HParams(
          use_dynamic_rnn=True,
          batch_size=BATCH_SIZE,
          lr=0.0002,
          l2_reg=2.5e-5,
          clip_norm=5,
          initial_learning_rate=0.5,
          decay_steps=1000,
          decay_rate=0.85,
          rnn_layer_sizes=[100],
          skip_first_n_losses=32,
          one_hot_length=NUM_CLASSES,
          exponentially_decay_learning_rate=True,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import random
  from magenta.contrib import training
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: default_hparams() and returns: contrib_training