code: def _multilabel_coverage_error_update(
      preds: Tensor, target: Tensor
  ) -> Tuple[Tensor, int]:
      """Accumulate state for coverage error."""
      offset = torch.zeros_like(preds)
      offset[target == 0] = (
          preds.min().abs() + 10
      )  # Any number >1 works
      preds_mod = preds + offset
      preds_min = preds_mod.min(dim=1)[0]
      coverage = (
          (preds >= preds_min[:, None]).sum(dim=1).to(torch.float32)
      )
      return coverage.sum(), coverage.numel()
  
file_path: torchmetrics/src/torchmetrics/functional/classification/ranking.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torchmetrics.functional.classification.confusion_matrix import _multilabel_confusion_matrix_arg_validation, _multilabel_confusion_matrix_format, _multilabel_confusion_matrix_tensor_validation
  from torchmetrics.utilities.data import _cumsum
label_desc: Use image node_id 4 for calling a global function with example usage: _multilabel_coverage_error_update(preds, target) and returns: coverage,  coverage