code: def invalid_range(
      cursor, replay_capacity, stack_size, update_horizon
  ):
      """Returns a array with the indices of cursor-related invalid transitions.
  
      There are update_horizon + stack_size invalid indices:
        - The update_horizon indices before the cursor, because we do not have a
          valid N-step transition (including the next state).
        - The stack_size indices on or immediately after the cursor.
      If N = update_horizon, K = stack_size, and the cursor is at c, invalid
      indices are:
        c - N, c - N + 1, ..., c, c + 1, ..., c + K - 1.
  
      It handles special cases in a circular buffer in the beginning and the end.
  
      Args:
        cursor: int, the position of the cursor.
        replay_capacity: int, the size of the replay memory.
        stack_size: int, the size of the stacks returned by the replay memory.
        update_horizon: int, the agent's update horizon.
      Returns:
        np.array of size stack_size with the invalid indices.
      """
      assert cursor < replay_capacity
      return np.array(
          [
              (cursor - update_horizon + i) % replay_capacity
              for i in range(stack_size + update_horizon)
          ]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import gzip
  import math
  import os
  import pickle
  from absl import logging
  import gin.tf
  import numpy
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: invalid_range(cursor, replay_capacity, stack_size, update_horizon) and returns: np