class_docstr: A TFUniformReplayBuffer with batched adds and uniform sampling.
class_docstr_tok: ["A","TFUniformReplayBuffer","with","batched","adds","and","uniform","sampling","."]
code: def as_dataset(
      self,
      sample_batch_size=None,
      num_steps=None,
      num_parallel_calls=None,
      single_deterministic_pass=False,
  ):
      return super(TFUniformReplayBuffer, self).as_dataset(
          sample_batch_size,
          num_steps,
          num_parallel_calls,
          single_deterministic_pass=single_deterministic_pass,
      )
  
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 9 for calling the TFUniformReplayBuffer obj's underlying member method code with example usage: obj.as_dataset(sample_batch_size, num_steps, num_parallel_calls, single_deterministic_pass) and returns: super