code: def SignificanceWeights(serializer, decay):
      """Multiplies a binary mask with a symbol significance mask."""
  
      def significance_weights(mask):
          # (repr,) -> (batch, length, repr)
          # significance = [0, 1, 2]
          significance = serializer.significance_map
          assert significance.shape[0] == mask.shape[2]
          # significance = batch_size * [0, 1, 2]
          significance = jnp.repeat(
              significance[np.newaxis, ...],
              repeats=mask.shape[0],
              axis=0,
          )
          # significance = batch_size * [0, 1, 2] * mask.shape[1]
          significance = jnp.repeat(
              significance[..., jnp.newaxis],
              repeats=mask.shape[1],
              axis=2,
          )
          # significance = batch_size *  mask.shape[1] * [0, 1, 2]
          significance = jnp.swapaxes(significance, 1, 2)
          assert significance.shape == mask.shape
          sig_weights = mask * decay**significance
          return sig_weights
  
      return tl.Fn("SignificanceWeights", significance_weights)
  
imports: import functools
  import gym
  import numpy
  from trax import layers
  from trax.fastmath import numpy
  from trax.rl import space_serializer
label_desc: Use image node_id 5 for calling a global function with example usage: SignificanceWeights(serializer, decay) and returns: tl, sig_weights