code: def _binary_hinge_loss_tensor_validation(
      preds: Tensor, target: Tensor, ignore_index: Optional[int] = None
  ) -> None:
      _binary_confusion_matrix_tensor_validation(
          preds, target, ignore_index
      )
      if not preds.is_floating_point():
          raise ValueError(
              "Expected argument `preds` to be floating tensor with probabilities/logits"
              f" but got tensor with dtype {preds.dtype}"
          )
  
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 3 for calling a global function with example usage: _binary_hinge_loss_tensor_validation(preds, target, ignore_index) without return types