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 clearing all the values at the given rows.
  
  Args:
    rows: A list/tensor of location(s) to clear values.
  
  Returns:
    Ops for clearing the values at rows.
docstr_tok: ["Returns","ops","for","clearing","all","the","values","at","the","given","rows",".","Args",":","rows",":","A","list\/tensor","of","location","(","s",")","to","clear","values",".","Returns",":","Ops","for","clearing","the","values","at","rows","."]
code: def clear_rows(self, rows):
      """Returns ops for clearing all the values at the given rows.
  
      Args:
        rows: A list/tensor of location(s) to clear values.
  
      Returns:
        Ops for clearing the values at rows.
      """
      rows = tf.convert_to_tensor(value=rows, dtype=tf.int64)
      clear_ops = []
      for spec, slot in zip(
          self._flattened_specs, self._flattened_slots
      ):
          new_value = tf.fill([tf.size(input=rows)], _empty_slot(spec))
          clear_ops.append(
              self._slot2variable_map[slot].insert_or_assign(
                  rows, new_value
              )
          )
      return tf.group(*clear_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 11 for calling the EpisodicTable obj's underlying member method code with example usage: obj.clear_rows(rows) and returns: tf