code: def check_confusion_matrix_metric_name(metric_name: str) -> str:
      """
      There are many metrics related to confusion matrix, and some of the metrics have
      more than one names. In addition, some of the names are very long.
      Therefore, this function is used to check and simplify the name.
  
      Returns:
          Simplified metric name.
  
      Raises:
          NotImplementedError: when the metric is not implemented.
      """
      metric_name = metric_name.replace(" ", "_")
      metric_name = metric_name.lower()
      if metric_name in [
          "sensitivity",
          "recall",
          "hit_rate",
          "true_positive_rate",
          "tpr",
      ]:
          return "tpr"
      if metric_name in [
          "specificity",
          "selectivity",
          "true_negative_rate",
          "tnr",
      ]:
          return "tnr"
      if metric_name in [
          "precision",
          "positive_predictive_value",
          "ppv",
      ]:
          return "ppv"
      if metric_name in ["negative_predictive_value", "npv"]:
          return "npv"
      if metric_name in ["miss_rate", "false_negative_rate", "fnr"]:
          return "fnr"
      if metric_name in ["fall_out", "false_positive_rate", "fpr"]:
          return "fpr"
      if metric_name in ["false_discovery_rate", "fdr"]:
          return "fdr"
      if metric_name in ["false_omission_rate", "for"]:
          return "for"
      if metric_name in ["prevalence_threshold", "pt"]:
          return "pt"
      if metric_name in [
          "threat_score",
          "critical_success_index",
          "ts",
          "csi",
      ]:
          return "ts"
      if metric_name in ["accuracy", "acc"]:
          return "acc"
      if metric_name in ["balanced_accuracy", "ba"]:
          return "ba"
      if metric_name in ["f1_score", "f1"]:
          return "f1"
      if metric_name in ["matthews_correlation_coefficient", "mcc"]:
          return "mcc"
      if metric_name in ["fowlkes_mallows_index", "fm"]:
          return "fm"
      if metric_name in [
          "informedness",
          "bookmaker_informedness",
          "bm",
          "youden_index",
          "youden",
      ]:
          return "bm"
      if metric_name in ["markedness", "deltap", "mk"]:
          return "mk"
      raise NotImplementedError("the metric is not implemented.")
  
file_path: MONAI/monai/metrics/confusion_matrix.py
imports: from __future__ import annotations
  import warnings
  from collections.abc import Sequence
  import torch
  from monai.metrics.utils import do_metric_reduction, ignore_background
  from monai.utils import MetricReduction, ensure_tuple
  from .metric import CumulativeIterationMetric
label_desc: Use image node_id 3 for calling a global function with example usage: check_confusion_matrix_metric_name(metric_name) and returns: str, str, str, str, str, str, str, str, str, str, str, str, str, str, str, str, str