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 ops for extending a set of rows by the given TensorLists.
  
  Args:
    rows: A batch of row locations to extend.
    episode_lists: Nested batch of TensorLists, must have the same batch
      dimension as rows.
  
  Returns:
    Ops for extending the table.
docstr_tok: ["Returns","ops","for","extending","a","set","of","rows","by","the","given","TensorLists",".","Args",":","rows",":","A","batch","of","row","locations","to","extend",".","episode_lists",":","Nested","batch","of","TensorLists",",","must","have","the","same","batch","dimension","as","rows",".","Returns",":","Ops","for","extending","the","table","."]
code: def extend(self, rows, episode_lists):
      """Returns ops for extending a set of rows by the given TensorLists.
  
      Args:
        rows: A batch of row locations to extend.
        episode_lists: Nested batch of TensorLists, must have the same batch
          dimension as rows.
  
      Returns:
        Ops for extending the table.
      """
      nest_utils.assert_same_structure(self.slots, episode_lists)
  
      rows = tf.convert_to_tensor(value=rows, dtype=tf.int64)
      existing_lists = self.get_episode_lists(rows)
      flat_existing_lists = tf.nest.flatten(existing_lists)
      flat_episode_lists = tf.nest.flatten(episode_lists)
  
      write_ops = []
      for spec, slot, existing_list, episode_list in zip(
          self._flattened_specs,
          self._flattened_slots,
          flat_existing_lists,
          flat_episode_lists,
      ):
          extended_list = list_ops.tensor_list_concat_lists(
              existing_list, episode_list, element_dtype=spec.dtype
          )
          slot_variable = self._slot2variable_map[slot]
          write_ops.append(
              slot_variable.insert_or_assign(rows, extended_list)
          )
      return tf.group(*write_ops)
  
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 9 for calling the EpisodicTable obj's underlying member method code with example usage: obj.extend(rows, episode_lists) and returns: tf