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 all values for the given row.
  
  Args:
    row: A scalar tensor of location to read values from. A batch of values
      will be returned with each Tensor having an extra first dimension equal
      to the length of rows.
  
  Returns:
    Stacked values at given row.
docstr_tok: ["Returns","all","values","for","the","given","row",".","Args",":","row",":","A","scalar","tensor","of","location","to","read","values","from",".","A","batch","of","values","will","be","returned","with","each","Tensor","having","an","extra","first","dimension","equal","to","the","length","of","rows",".","Returns",":","Stacked","values","at","given","row","."]
code: def get_episode_values(self, row):
      """Returns all values for the given row.
  
      Args:
        row: A scalar tensor of location to read values from. A batch of values
          will be returned with each Tensor having an extra first dimension equal
          to the length of rows.
  
      Returns:
        Stacked values at given row.
      """
      row = tf.convert_to_tensor(value=row, dtype=tf.int64)
      row.shape.assert_has_rank(0)
      return tf.nest.map_structure(
          self._stack_tensor_list,
          self.slots,
          self.get_episode_lists(row),
      )
  
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 6 for calling the EpisodicTable obj's underlying member method code with example usage: obj.get_episode_values(row) and returns: tf