class_docstr: Wrapper for batching metrics.
  
  This can be used to wrap any python metric that takes a single trajectory to
  produce a batched version of the metric that takes a batch of trajectories.
class_docstr_tok: ["Wrapper","for","batching","metrics",".","This","can","be","used","to","wrap","any","python","metric","that","takes","a","single","trajectory","to","produce","a","batched","version","of","the","metric","that","takes","a","batch","of","trajectories","."]
docstr: Evaluates the current value of the metric.
docstr_tok: ["Evaluates","the","current","value","of","the","metric","."]
code: def result(self) -> Any:
      """Evaluates the current value of the metric."""
      if self._built:
          return self._metric_class.aggregate(self._metrics)
      else:
          return np.array(0.0, dtype=self._dtype)
  
file_path: agents/tf_agents/metrics/batched_py_metric.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any, Optional, Text
  import numpy
  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
label_desc: Use image node_id 5 for calling the BatchedPyMetric obj's underlying member method code with example usage: obj.result() and returns: self, np