code: def binary_roc(
      preds: Tensor,
      target: Tensor,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
  ) -> Tuple[Tensor, Tensor, Tensor]:
      r"""Compute the Receiver Operating Characteristic (ROC) for binary tasks.
  
      The curve consist of multiple pairs of true positive rate (TPR) and false positive rate (FPR) values evaluated at
      different thresholds, such that the tradeoff between the two values can be seen.
  
      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.
  
      The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
      that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
      non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
      argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
      size :math:`\mathcal{O}(n_{thresholds})` (constant memory).
  
      Note that outputted thresholds will be in reversed order to ensure that they corresponds to both fpr and tpr which
      are sorted in reversed order during their calculation, such that they are monotome increasing.
  
      Args:
          preds: Tensor with predictions
          target: Tensor with true labels
          thresholds:
              Can be one of:
  
              - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
                all the data. Most accurate but also most memory consuming approach.
              - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
                0 to 1 as bins for the calculation.
              - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
              - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
                bins for the calculation.
  
          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.
  
      Returns:
          (tuple): a tuple of 3 tensors containing:
  
          - fpr: an 1d tensor of size (n_thresholds+1, ) with false positive rate values
          - tpr: an 1d tensor of size (n_thresholds+1, ) with true positive rate values
          - thresholds: an 1d tensor of size (n_thresholds, ) with decreasing threshold values
  
      Example:
          >>> from torchmetrics.functional.classification import binary_roc
          >>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
          >>> target = torch.tensor([0, 1, 1, 0])
          >>> binary_roc(preds, target, thresholds=None)  # doctest: +NORMALIZE_WHITESPACE
          (tensor([0.0000, 0.5000, 0.5000, 0.5000, 1.0000]),
           tensor([0.0000, 0.0000, 0.5000, 1.0000, 1.0000]),
           tensor([1.0000, 0.8000, 0.7000, 0.5000, 0.0000]))
          >>> binary_roc(preds, target, thresholds=5)  # doctest: +NORMALIZE_WHITESPACE
          (tensor([0.0000, 0.5000, 0.5000, 0.5000, 1.0000]),
           tensor([0., 0., 1., 1., 1.]),
           tensor([1.0000, 0.7500, 0.5000, 0.2500, 0.0000]))
  
      """
      if validate_args:
          _binary_precision_recall_curve_arg_validation(
              thresholds, ignore_index
          )
          _binary_precision_recall_curve_tensor_validation(
              preds, target, ignore_index
          )
      preds, target, thresholds = _binary_precision_recall_curve_format(
          preds, target, thresholds, ignore_index
      )
      state = _binary_precision_recall_curve_update(
          preds, target, thresholds
      )
      return _binary_roc_compute(state, thresholds)
  
file_path: torchmetrics/src/torchmetrics/functional/classification/roc.py
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.precision_recall_curve import _binary_clf_curve, _binary_precision_recall_curve_arg_validation, _binary_precision_recall_curve_format, _binary_precision_recall_curve_tensor_validation, _binary_precision_recall_curve_update, _multiclass_precision_recall_curve_arg_validation, _multiclass_precision_recall_curve_format, _multiclass_precision_recall_curve_tensor_validation, _multiclass_precision_recall_curve_update, _multilabel_precision_recall_curve_arg_validation, _multilabel_precision_recall_curve_format, _multilabel_precision_recall_curve_tensor_validation, _multilabel_precision_recall_curve_update
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.compute import _safe_divide, interp
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 2 for calling a global function with example usage: binary_roc(preds, target, thresholds, ignore_index, validate_args) and returns: _binary_roc_compute