class_docstr: Abstract class to calculate the TP, FP, FN and runs an evaluating function on the result.
  
  Parameters
  ----------
  iou_thres: float, default: 0.5
      IoU below this threshold will be ignored.
  confidence_thres: float, default: 0.5
      Confidence below this threshold will be ignored.
  evaluating_function: Union[Callable, str], default: "recall"
      will run on each class result i.e `func(tp, fp, fn)`
  averaging_method : str, default: 'per_class'
      Determines which averaging method to apply, possible values are:
      'per_class': Return a np array with the scores for each class (sorted by class name).
      'binary': Returns the score for the positive class. Should be used only in binary classification cases.
      'micro': Returns the micro-averaged score.
      'macro': Returns the mean of scores per class.
      'weighted': Returns a weighted mean of scores based of the class size in y_true.
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docstr: Reset metric state.
docstr_tok: ["Reset","metric","state","."]
code: def reset(self):
      """Reset metric state."""
      super().reset()
      self._evals = defaultdict(lambda: {"tp": 0, "fp": 0, "fn": 0})
  
imports: import typing
  from collections import defaultdict
  import numpy
  from ignite.metrics import Metric
  from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce
  from deepchecks.utils.metrics import averaging_mechanism
  from deepchecks.vision.metrics_utils.confusion_matrix_counts_metrics import AVAILABLE_EVALUATING_FUNCTIONS
  from deepchecks.vision.metrics_utils.metric_mixin import MetricMixin, ObjectDetectionMetricMixin
  from deepchecks.vision.vision_data.utils import is_torch_object
label_desc: Use image node_id 2 for calling the TpFpFn obj's underlying member method code with example usage: obj.reset() without return types