code: def calculate_metric(self):
      scores_list: List[float] = []
      preds_list: List[int] = []
      targets_list: List[int] = []
  
      for scores, preds, targets in zip(
          self.all_scores, self.all_preds, self.all_targets
      ):
          non_pad_idxs = [
              idx
              for (idx, target) in enumerate(targets)
              if target != self.pad_index
          ]
  
          scores = [scores[idx] for idx in non_pad_idxs]
          preds = [preds[idx] for idx in non_pad_idxs]
          targets = [targets[idx] for idx in non_pad_idxs]
  
          assert len(scores) == len(preds) == len(targets)
  
          scores_list.extend(scores)
          preds_list.extend(preds)
          targets_list.extend(targets)
  
      label_predictions: List[LabelPrediction] = [
          LabelPrediction(scores, pred, target)
          for (scores, pred, target) in zip(
              scores_list, preds_list, targets_list
          )
      ]
  
      calibration_metrics = compute_calibration(label_predictions)
  
      return calibration_metrics
  
file_path: pytext/pytext/metric_reporters/calibration_metric_reporter.py
imports: from typing import Any, Dict, List
  from pytext.config import PyTextConfig
  from pytext.metric_reporters.channel import Channel, ConsoleChannel
  from pytext.metrics import LabelPrediction
  from pytext.metrics.calibration_metrics import compute_calibration
  from torch import Tensor
  from .metric_reporter import MetricReporter
label_desc: Use image node_id 6 for calling the CalibrationMetricReporter obj's underlying member method code with example usage: obj.calculate_metric() and returns: calibration_metrics