code: def binary_hinge_loss(
      preds: Tensor,
      target: Tensor,
      squared: bool = False,
      ignore_index: Optional[int] = None,
      validate_args: bool = False,
  ) -> Tensor:
      r"""Compute the mean `Hinge loss`_ typically used for Support Vector Machines (SVMs) for binary tasks.
  
      .. math::
          \text{Hinge loss} = \max(0, 1 - y \times \hat{y})
  
      Where :math:`y \in {-1, 1}` is the target, and :math:`\hat{y} \in \mathbb{R}` is the prediction.
  
      Accepts the following input tensors:
  
      - ``preds`` (float tensor): ``(N, ...)``. Preds should be a tensor containing probabilities or logits for each
        observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
        sigmoid per element.
      - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
        only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
  
      Additional dimension ``...`` will be flattened into the batch dimension.
  
      Args:
          preds: Tensor with predictions
          target: Tensor with true labels
          squared:
              If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
          ignore_index:
              Specifies a target value that is ignored and does not contribute to the metric calculation
          validate_args: bool indicating if input arguments and tensors should be validated for correctness.
              Set to ``False`` for faster computations.
  
      Example:
          >>> from torch import tensor
          >>> from torchmetrics.functional.classification import binary_hinge_loss
          >>> preds = tensor([0.25, 0.25, 0.55, 0.75, 0.75])
          >>> target = tensor([0, 0, 1, 1, 1])
          >>> binary_hinge_loss(preds, target)
          tensor(0.6900)
          >>> binary_hinge_loss(preds, target, squared=True)
          tensor(0.6905)
  
      """
      if validate_args:
          _binary_hinge_loss_arg_validation(squared, ignore_index)
          _binary_hinge_loss_tensor_validation(
              preds, target, ignore_index
          )
      preds, target = _binary_confusion_matrix_format(
          preds,
          target,
          threshold=0.0,
          ignore_index=ignore_index,
          convert_to_labels=False,
      )
      measures, total = _binary_hinge_loss_update(
          preds, target, squared
      )
      return _hinge_loss_compute(measures, total)
  
file_path: torchmetrics/src/torchmetrics/functional/classification/hinge.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.confusion_matrix import _binary_confusion_matrix_format, _binary_confusion_matrix_tensor_validation, _multiclass_confusion_matrix_format, _multiclass_confusion_matrix_tensor_validation
  from torchmetrics.utilities.data import to_onehot
  from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
label_desc: Use image node_id 5 for calling a global function with example usage: binary_hinge_loss(preds, target, squared, ignore_index, validate_args) and returns: _hinge_loss_compute