class_docstr: Computes PWIL rewards along a trajectory.
  
  The rewards measure similarity to the demonstration transitions and are based
  on a greedy approximation to the Wasserstein distance between trajectories.
docstr: Makes all expert transitions available and initialize weights.
code: def reset(self) -> None:
      """Makes all expert transitions available and initialize weights."""
      num_expert_atoms = len(self.expert_atoms)
      self._all_expert_weights_zero = False
      self.expert_weights = np.ones(num_expert_atoms) / num_expert_atoms
  
file_path: acme/acme/agents/jax/pwil/rewarder.py
imports: from typing import Iterator
  from acme import types
  import jax
  import jax.numpy
  import numpy
label_desc: Use image node_id 3 for calling the WassersteinDistanceRewarder obj's underlying member method code with example usage: obj.reset() without return types