class_docstr: A replay buffer that stores and can sample whole episodes.
  
  This buffer only stores valid steps, and doesn't require paths to
  have a maximum length.
  
  Args:
      capacity_in_transitions (int): Total memory allocated for the buffer.
      env_spec (EnvSpec): Environment specification.
class_docstr_tok: ["A","replay","buffer","that","stores","and","can","sample","whole","episodes",".","This","buffer","only","stores","valid","steps",",","and","does","n't","require","paths","to","have","a","maximum","length",".","Args",":","capacity_in_transitions","(","int",")",":","Total","memory","allocated","for","the","buffer",".","env_spec","(","EnvSpec",")",":","Environment","specification","."]
docstr: Return the size of the replay buffer.
  
  Returns:
      int: Size of the current replay buffer.
docstr_tok: ["Return","the","size","of","the","replay","buffer",".","Returns",":","int",":","Size","of","the","current","replay","buffer","."]
code: def n_transitions_stored(self):
      """Return the size of the replay buffer.
  
      Returns:
          int: Size of the current replay buffer.
  
      """
      return int(self._transitions_stored)
  
file_path: garage/src/garage/replay_buffer/path_buffer.py
imports: import collections
  import numpy
  from garage import StepType, TimeStepBatch
label_desc: Use image node_id 12 for calling the PathBuffer obj's underlying member method code with example usage: obj.n_transitions_stored() and returns: int