class_docstr: A table that can store Episodes of variable length.
class_docstr_tok: ["A","table","that","can","store","Episodes","of","variable","length","."]
docstr: Returns episodes as TensorLists.
  
  Args:
    rows: A list/tensor of location(s) to retrieve. If not specified, all
      episodes are returned.
  
  Returns:
    Episodes as TensorLists, stored in nested Tensors.
docstr_tok: ["Returns","episodes","as","TensorLists",".","Args",":","rows",":","A","list\/tensor","of","location","(","s",")","to","retrieve",".","If","not","specified",",","all","episodes","are","returned",".","Returns",":","Episodes","as","TensorLists",",","stored","in","nested","Tensors","."]
code: def get_episode_lists(self, rows=None):
      """Returns episodes as TensorLists.
  
      Args:
        rows: A list/tensor of location(s) to retrieve. If not specified, all
          episodes are returned.
  
      Returns:
        Episodes as TensorLists, stored in nested Tensors.
      """
      if rows is None:
          rows = tf.range(self._capacity, dtype=tf.int64)
      else:
          rows = tf.convert_to_tensor(value=rows, dtype=tf.int64)
      values = [
          self._slot2variable_map[slot].lookup(rows)
          for slot in self._flattened_slots
      ]
      return tf.nest.pack_sequence_as(self.slots, values)
  
file_path: agents/tf_agents/replay_buffers/episodic_table.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.utils import nest_utils
  from tensorflow.python.ops import list_ops
label_desc: Use image node_id 5 for calling the EpisodicTable obj's underlying member method code with example usage: obj.get_episode_lists(rows) and returns: tf