class_docstr: `Metrics` is wrapper of popular metrics implementation used to evaluate the
  current prediction's performance by comparing it with the labels.
  
  Args:
      metric_name (`str`):
          Indicate which metric is used to evalute.
class_docstr_tok: ["`","Metrics","`","is","wrapper","of","popular","metrics","implementation","used","to","evaluate","the","current","prediction","'s","performance","by","comparing","it","with","the","labels",".","Args",":","metric_name","(","`","str","`",")",":","Indicate","which","metric","is","used","to","evalute","."]
docstr: Reset the internal status of 'Metrics'.
docstr_tok: ["Reset","the","internal","status","of","'Metrics","'","."]
code: def reset(self) -> None:
      """
      Reset the internal status of 'Metrics'.
      """
      raise NotImplementedError
  
imports: from typing import Any, Dict
  import torchmetrics
  from torchmetrics.detection.map import MeanAveragePrecision
label_desc: Use image node_id 5 for calling the Metrics obj's underlying member method code with example usage: obj.reset() without return types