class_docstr: Abstract class for Replay Buffer.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      size_in_transitions (int): total size of transitions in the buffer
      time_horizon (int): time horizon of epsiode.
docstr: Convert the shape of episode_buffer.
  
  episode_buffer: {time_horizon, algo.episode_batch_size, flat_dim}.
  buffer: {size, time_horizon, flat_dim}.
  
  Returns:
      dict: Transitions that have been formated to fit properly in this
          replay buffer.
docstr_tok: ["Convert","the","shape","of","episode_buffer",".","episode_buffer",":","{","time_horizon",",","algo.episode_batch_size",",","flat_dim","}",".","buffer",":","{","size",",","time_horizon",",","flat_dim","}",".","Returns",":","dict",":","Transitions","that","have","been","formated","to","fit","properly","in","this","replay","buffer","."]
code: def _convert_episode_to_batch_major(self):
      """Convert the shape of episode_buffer.
  
      episode_buffer: {time_horizon, algo.episode_batch_size, flat_dim}.
      buffer: {size, time_horizon, flat_dim}.
  
      Returns:
          dict: Transitions that have been formated to fit properly in this
              replay buffer.
  
      """
      transitions = {}
      for key in self._episode_buffer:
          val = np.array(self._episode_buffer[key])
          transitions[key] = val.swapaxes(0, 1)
      return transitions
  
file_path: garage/src/garage/replay_buffer/replay_buffer.py
imports: import abc
  from abc import abstractmethod
  import numpy
label_desc: Use image node_id 8 for calling the ReplayBuffer obj's underlying member method code with example usage: obj._convert_episode_to_batch_major() and returns: transitions