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: Resets internal stat gathering variables used to compute the metric.
docstr_tok: ["Resets","internal","stat","gathering","variables","used","to","compute","the","metric","."]
code: def reset(self):
      """Resets internal stat gathering variables used to compute the metric."""
      if self._built:
          for metric in self._metrics:
              metric.reset()
  
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 4 for calling the BatchedPyMetric obj's underlying member method code with example usage: obj.reset() without return types