code: def compute_topk_classification_metrics(
      predictions: Sequence[LabelTopKPrediction],
      label_names: Sequence[str],
      loss: float,
      log_per_label_metrics: bool = True,
  ) -> ClassificationMetrics:
      """
      A general function that computes classification metrics given a list of label predictions.
  
      Args:
          predictions: Label predictions, including the confidence score for each label.
          label_names: Indexed label names.
          loss: Calculated loss.
          log_per_label_metrics: Report macro prf1 values per label.
  
      Returns:
          ClassificationMetrics which contains various classification metrics.
      """
      num_correct = 0
      per_label_confusions = PerLabelConfusions()
      for _, topk, label in predictions:
          predicted_labels = list(map(label_names.__getitem__, topk))
          expected_label = label_names[label]
  
          if expected_label in predicted_labels:
              num_correct += 1
              per_label_confusions.update(expected_label, "TP", 1)
          else:
              per_label_confusions.update(expected_label, "FN", 1)
              per_label_confusions.update(predicted_labels[0], "FP", 1)
  
      macro_prf1_metrics = per_label_confusions.compute_metrics(
          log_per_label_metrics=log_per_label_metrics
      )
  
      return ClassificationMetrics(
          accuracy=safe_division(num_correct, len(predictions)),
          macro_prf1_metrics=macro_prf1_metrics,
          per_label_soft_scores={},
          mcc=None,
          roc_auc=None,
          loss=loss,
      )
  
imports: from enum import Enum
  from itertools import tee, zip_longest
  from typing import Generator, List, Optional, Sequence
  import torch
  from pytext.common.constants import Stage
  from pytext.data import CommonMetadata
  from pytext.metrics import ClassificationMetrics, compute_classification_metrics, compute_multi_label_classification_metrics, LabelListPrediction, LabelPrediction, LabelTopKPrediction, RECALL_AT_PRECISION_THRESHOLDS, safe_division
  from pytext.metrics.intent_slot_metrics import PerLabelConfusions
  from .channel import Channel, ConsoleChannel, FileChannel
  from .metric_reporter import MetricReporter
label_desc: Use image node_id 1 for calling a global function with example usage: compute_topk_classification_metrics(predictions, label_names, loss, log_per_label_metrics) and returns: ClassificationMetrics