code: def get_default_hparams():
      """Return default HParams for sketch-rnn."""
      hparams = contrib_training.HParams(
          data_set=["aaron_sheep.npz"],  # Our dataset.
          num_steps=10000000,  # Total number of steps of training. Keep large.
          save_every=500,  # Number of batches per checkpoint creation.
          max_seq_len=250,  # Not used. Will be changed by model. [Eliminate?]
          dec_rnn_size=512,  # Size of decoder.
          dec_model="lstm",  # Decoder: lstm, layer_norm or hyper.
          enc_rnn_size=256,  # Size of encoder.
          enc_model="lstm",  # Encoder: lstm, layer_norm or hyper.
          z_size=128,  # Size of latent vector z. Recommend 32, 64 or 128.
          kl_weight=0.5,  # KL weight of loss equation. Recommend 0.5 or 1.0.
          kl_weight_start=0.01,  # KL start weight when annealing.
          kl_tolerance=0.2,  # Level of KL loss at which to stop optimizing for KL.
          batch_size=100,  # Minibatch size. Recommend leaving at 100.
          grad_clip=1.0,  # Gradient clipping. Recommend leaving at 1.0.
          num_mixture=20,  # Number of mixtures in Gaussian mixture model.
          learning_rate=0.001,  # Learning rate.
          decay_rate=0.9999,  # Learning rate decay per minibatch.
          kl_decay_rate=0.99995,  # KL annealing decay rate per minibatch.
          min_learning_rate=0.00001,  # Minimum learning rate.
          use_recurrent_dropout=True,  # Dropout with memory loss. Recommended
          recurrent_dropout_prob=0.90,  # Probability of recurrent dropout keep.
          use_input_dropout=False,  # Input dropout. Recommend leaving False.
          input_dropout_prob=0.90,  # Probability of input dropout keep.
          use_output_dropout=False,  # Output dropout. Recommend leaving False.
          output_dropout_prob=0.90,  # Probability of output dropout keep.
          random_scale_factor=0.15,  # Random scaling data augmentation proportion.
          augment_stroke_prob=0.10,  # Point dropping augmentation proportion.
          conditional=True,  # When False, use unconditional decoder-only model.
          is_training=True,  # Is model training? Recommend keeping true.
      )
      return hparams
  
file_path: magenta/magenta/models/sketch_rnn/model.py
imports: import random
  from magenta.contrib import training
  from magenta.models.sketch_rnn import rnn
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: get_default_hparams() and returns: hparams