code: def _discordant_element_sum(x: Tensor, y: Tensor, i: int) -> Tensor:
      """Count a total number of discordant pairs in a single sequences."""
      return (
          torch.logical_or(
              torch.logical_and(
                  x[i] > x[(i + 1) :], y[i] < y[(i + 1) :]
              ),
              torch.logical_and(
                  x[i] < x[(i + 1) :], y[i] > y[(i + 1) :]
              ),
          )
          .sum(0)
          .unsqueeze(0)
      )
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.regression.utils import _check_data_shape_to_num_outputs
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.data import _bincount, _cumsum, dim_zero_cat
  from torchmetrics.utilities.enums import EnumStr
label_desc: Use image node_id 4 for calling a global function with example usage: _discordant_element_sum(x, y, i) and returns: torch