code: def muzero_pair(
      min_value: float, max_value: float, num_bins: int, tx: TxPair
  ) -> TxPair:
      """Create the transformation pair introduced in MuZero.
  
      This more complex pair of transformations, combines a monotonic squashing
      function with a reparametrisation of the value over a fixed support.
  
      See "Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model"
      by Schrittwieser et al. (https://arxiv.org/abs/1911.08265).
  
      Args:
        min_value: minimum value of the discrete support.
        max_value: maximum value of the discrete support.
        num_bins: number of discrete bins used by the fixed support.
        tx: non-linear transformation to be applied before reparametrisation.
  
      Returns:
        a transformation pair.
      """
      return compose_tx(tx, twohot_pair(min_value, max_value, num_bins))
  
file_path: rlax/rlax/_src/nonlinear_bellman.py
imports: import collections
  import functools
  import chex
  import jax.numpy
  from rlax._src import base
  from rlax._src import multistep
  from rlax._src import transforms
label_desc: Use image node_id 3 for calling a global function with example usage: muzero_pair(min_value, max_value, num_bins, tx) and returns: compose_tx