code: def _generate_plausible_inputs_multilabel(
      num_classes=NUM_CLASSES,
      num_batches=NUM_BATCHES,
      batch_size=BATCH_SIZE,
  ):
      correct_targets = torch.randint(
          high=num_classes, size=(num_batches, batch_size)
      )
      preds = torch.rand(num_batches, batch_size, num_classes)
      targets = torch.zeros_like(preds, dtype=torch.long)
      for i in range(preds.shape[0]):
          for j in range(preds.shape[1]):
              targets[i, j, correct_targets[i, j]] = 1
      preds += (
          torch.rand(num_batches, batch_size, num_classes) * targets / 3
      )
  
      preds = preds / preds.sum(dim=2, keepdim=True)
  
      return _Input(preds=preds, target=targets)
  
file_path: torchmetrics/tests/unittests/classification/inputs.py
imports: from typing import Any
  import pytest
  import torch
  from torch import Tensor
  from unittests import BATCH_SIZE, EXTRA_DIM, NUM_BATCHES, NUM_CLASSES, _GroupInput, _Input
  from unittests.helpers import seed_all
label_desc: Use image node_id 4 for calling a global function with example usage: _generate_plausible_inputs_multilabel(num_classes, num_batches, batch_size) and returns: _Input