code: def _stat_scores_compute(
      tp: Tensor, fp: Tensor, tn: Tensor, fn: Tensor
  ) -> Tensor:
      """Compute the number of true positives, false positives, true negatives, false negatives.
  
      Concatenates the input tensors along with the support into one output.
  
      Args:
          tp: True positives
          fp: False positives
          tn: True negatives
          fn: False negatives
  
      """
      stats = [
          tp.unsqueeze(-1),
          fp.unsqueeze(-1),
          tn.unsqueeze(-1),
          fn.unsqueeze(-1),
          tp.unsqueeze(-1) + fn.unsqueeze(-1),  # support
      ]
      outputs: Tensor = torch.cat(stats, -1)
      return torch.where(
          outputs < 0, tensor(-1, device=outputs.device), outputs
      )
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics.utilities.checks import _check_same_shape, _input_format_classification
  from torchmetrics.utilities.data import _bincount, select_topk
  from torchmetrics.utilities.enums import AverageMethod, ClassificationTask, DataType, MDMCAverageMethod
label_desc: Use image node_id 23 for calling a global function with example usage: _stat_scores_compute(tp, fp, tn, fn) and returns: torch