class_docstr: Provides lists with a method to find multiple elements.
  
  This class is used for the timestep mapping which is central to
  the multi-agent episode. For each agent the timestep mapping is
  implemented with an `IndexMapping`.
  
  The `IndexMapping.find_indices` method simplifies the search for
  multiple environment timesteps at which some agents have stepped.
  See for example `MultiAgentEpisode.get_observations()`.
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code: def find_indices_right_equal(self, threshold: int, shift: bool = 0):
      indices = []
      for num in reversed(self):
          # To avoid duplicate indices in rare cases we have to test
          # for positive values. Note, `shift` could be negative.
          if num < threshold:
              # `_IndexMapping` is always ordered. Avoid to run through
              # all timesteps (could be thousands).
              break
          # Avoid negative indices, but as list is reversed: continue.
          elif self.index(num) + shift < 0:
              continue
          else:
              indices.append(max(self.index(num) + shift, 0))
      return list(reversed(indices))
  
imports: from collections import defaultdict
  from queue import Queue
  from typing import Any, Dict, List, Optional, Set, Union
  import uuid
  import numpy
  from ray.rllib.env.single_agent_episode import SingleAgentEpisode
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils.typing import MultiAgentDict
label_desc: Use image node_id 3 for calling the _IndexMapping obj's underlying member method code with example usage: obj.find_indices_right_equal(threshold, shift) and returns: list