class_docstr: An episodic ReplayBuffer with Uniform sampling.
class_docstr_tok: ["An","episodic","ReplayBuffer","with","Uniform","sampling","."]
docstr: Returns all the items in buffer.
  
  Returns:
    Returns all the items currently in the buffer. Returns a tensor
    of shape [1, SUM(T_i), ...]. Since episodes can be of different lengths,
    all steps of all episodes are grouped into one batch. Thus the first
    dimension is batch size = 1, the second dimension is of size equal to
    the sum of all timesteps for all episodes (SUM(T_i) for i in episode_ids).
    The remaining dimensions are the shape of the spec of items in the buffer.
code: def gather_all(self):
      """Returns all the items in buffer.
  
      Returns:
        Returns all the items currently in the buffer. Returns a tensor
        of shape [1, SUM(T_i), ...]. Since episodes can be of different lengths,
        all steps of all episodes are grouped into one batch. Thus the first
        dimension is batch size = 1, the second dimension is of size equal to
        the sum of all timesteps for all episodes (SUM(T_i) for i in episode_ids).
        The remaining dimensions are the shape of the spec of items in the buffer.
      """
      items, _ = self._gather_all()
      return items
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import gin
  import tensorflow
  from tf_agents import specs
  from tf_agents.replay_buffers import episodic_table
  from tf_agents.replay_buffers import replay_buffer
  from tf_agents.replay_buffers import table
  from tf_agents.utils import common
  from tensorflow.python.data.util import nest
  from tensorflow.python.layers import utils
  from tensorflow.python.ops import list_ops
  from tensorflow.python.ops.distributions import util
label_desc: Use image node_id 8 for calling the EpisodicReplayBuffer obj's underlying member method code with example usage: obj.gather_all() and returns: items