code: def get_anneal_mask(hparams):
      """Get anneal and kl mask."""
      startup = hparams.kl_startup_steps
      anneal = hparams.kl_anneal_steps
      global_step = tf.train.get_global_step()
      min_value = hparams.anneal_min_value
      step = tf.maximum(global_step - startup, 0)
      anneal = common_layers.inverse_lin_decay(
          anneal, min_value=min_value, step=step
      )
      kl_mask = tf.less(startup, tf.to_int32(global_step))
      kl_mask = tf.cast(kl_mask, tf.float32)
      return anneal, kl_mask
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import transformer_glow_layers_ops
  from tensor2tensor.models.transformer import transformer_decoder_layer
  from tensor2tensor.models.transformer import transformer_encoder
  from tensor2tensor.models.transformer import transformer_prepare_encoder
  from tensor2tensor.utils import learning_rate
  from tensor2tensor.utils import mlperf_log
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 15 for calling a global function with example usage: get_anneal_mask(hparams) and returns: anneal,  kl_mask