class_docstr: Computes the average undiscounted reward.
docstr: Creates an AverageReturnMetric.
docstr_tok: ["Creates","an","AverageReturnMetric","."]
code: def __init__(
      self,
      name: Text = "AverageReturn",
      buffer_size: types.Int = 10,
      batch_size: Optional[types.Int] = None,
  ):
      """Creates an AverageReturnMetric."""
      self._np_state = numpy_storage.NumpyState()
      # Set a dummy value on self._np_state.episode_return so it gets included in
      # the first checkpoint (before metric is first called).
      self._np_state.episode_return = np.float64(0)
      super(AverageReturnMetric, self).__init__(
          name, buffer_size=buffer_size, batch_size=batch_size
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  from typing import Any, Iterable, Optional, Text
  import gin
  import numpy
  import six
  from tf_agents.metrics import py_metric
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
  from tf_agents.utils import nest_utils
  from tf_agents.utils import numpy_storage
label_desc: Use image node_id 1 to create a new AverageReturnMetric object from inherited base classes: StreamingMetric with example: obj = AverageReturnMetric(name, buffer_size, batch_size)