code: def get_default_hparams():
      return contrib_training.HParams(
          max_seq_len=32,  # Maximum sequence length. Others will be truncated.
          z_size=32,  # Size of latent vector z.
          free_bits=0.0,  # Bits to exclude from KL loss per dimension.
          max_beta=1.0,  # Maximum KL cost weight, or cost if not annealing.
          beta_rate=0.0,  # Exponential rate at which to anneal KL cost.
          batch_size=512,  # Minibatch size.
          grad_clip=1.0,  # Gradient clipping. Recommend leaving at 1.0.
          clip_mode="global_norm",  # value or global_norm.
          # If clip_mode=global_norm and global_norm is greater than this value,
          # the gradient will be clipped to 0, effectively ignoring the step.
          grad_norm_clip_to_zero=10000,
          learning_rate=0.001,  # Learning rate.
          decay_rate=0.9999,  # Learning rate decay per minibatch.
          min_learning_rate=0.00001,  # Minimum learning rate.
      )
  
imports: import abc
  from magenta.contrib import training
  import tensorflow.compat.v1
  import tensorflow_probability
  import tf_slim
label_desc: Use image node_id 1 for calling a global function with example usage: get_default_hparams() and returns: contrib_training