class_docstr: A TFUniformReplayBuffer with batched adds and uniform sampling.
class_docstr_tok: ["A","TFUniformReplayBuffer","with","batched","adds","and","uniform","sampling","."]
docstr: Make a batch_size length list of tensors, with row ids for write.
docstr_tok: ["Make","a","batch_size","length","list","of","tensors",",","with","row","ids","for","write","."]
code: def _get_rows_for_id(self, id_):
      """Make a batch_size length list of tensors, with row ids for write."""
      id_mod = tf.math.mod(id_, self._max_length)
      rows = self._batch_offsets + id_mod
      return rows
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import gin
  import numpy
  import tensorflow
  from tf_agents.replay_buffers import replay_buffer
  from tf_agents.replay_buffers import table
  from tf_agents.specs import tensor_spec
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 16 for calling the TFUniformReplayBuffer obj's underlying member method code with example usage: obj._get_rows_for_id(id_) and returns: rows