class_docstr: A simple out-of-graph Replay Buffer.
  
  Stores transitions, state, action, reward, next_state, terminal (and any
  extra contents specified) in a circular buffer and provides a uniform
  transition sampling function.
  
  When the states consist of stacks of observations storing the states is
  inefficient. This class writes observations and constructs the stacked states
  at sample time.
  
  Attributes:
    add_count: int, counter of how many transitions have been added (including
      the blank ones at the beginning of an episode).
    invalid_range: np.array, an array with the indices of cursor-related invalid
      transitions
    episode_end_indices: set[int], a set of indices corresponding to the
      end of an episode.
docstr: Creates the numpy arrays used to store transitions.
      
docstr_tok: ["Creates","the","numpy","arrays","used","to","store","transitions","."]
code: def _create_storage(self):
      """Creates the numpy arrays used to store transitions."""
      self._store = {}
      for storage_element in self.get_storage_signature():
          array_shape = [self._replay_capacity] + list(
              storage_element.shape
          )
          self._store[storage_element.name] = np.empty(
              array_shape, dtype=storage_element.type
          )
  
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 3 for calling the OutOfGraphReplayBuffer obj's underlying member method code with example usage: obj._create_storage() without return types