code: def _get_add_op(spec, replay_buffer, batch_size):
      # TODO(b/68398658) Remove dtypes once scatter_update is fixed.
      action = tf.constant(
          1 * np.ones(spec[0].shape.as_list(), dtype=np.float32)
      )
      lidar = tf.constant(
          2 * np.ones(spec[1][0].shape.as_list(), dtype=np.float32)
      )
      camera = tf.constant(
          3 * np.ones(spec[1][1].shape.as_list(), dtype=np.float32)
      )
      values = [action, [lidar, camera]]
      values_batched = tf.nest.map_structure(
          lambda t: tf.stack([t] * batch_size), values
      )
  
      return values, replay_buffer.add_batch(values_batched)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents import specs
  from tf_agents.replay_buffers import tf_uniform_replay_buffer
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 1 for calling a global function with example usage: _get_add_op(spec, replay_buffer, batch_size) and returns: values,  replay_buffer