code: def accuracy(
      preds: Tensor,
      target: Tensor,
      task: Literal["binary", "multiclass", "multilabel"],
      threshold: float = 0.5,
      num_classes: Optional[int] = None,
      num_labels: Optional[int] = None,
      average: Literal["micro", "macro", "weighted", "none"] = "micro",
      multidim_average: Literal["global", "samplewise"] = "global",
      top_k: Optional[int] = 1,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
  ) -> Tensor:
      r"""Compute `Accuracy`_.
  
      .. math::
          \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)
  
      Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
  
      This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
      ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
      :func:`~torchmetrics.functional.classification.binary_accuracy`,
      :func:`~torchmetrics.functional.classification.multiclass_accuracy` and
      :func:`~torchmetrics.functional.classification.multilabel_accuracy` for the specific details of
      each argument influence and examples.
  
      Legacy Example:
          >>> from torch import tensor
          >>> target = tensor([0, 1, 2, 3])
          >>> preds = tensor([0, 2, 1, 3])
          >>> accuracy(preds, target, task="multiclass", num_classes=4)
          tensor(0.5000)
  
          >>> target = tensor([0, 1, 2])
          >>> preds = tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]])
          >>> accuracy(preds, target, task="multiclass", num_classes=3, top_k=2)
          tensor(0.6667)
  
      """
      task = ClassificationTask.from_str(task)
  
      if task == ClassificationTask.BINARY:
          return binary_accuracy(
              preds,
              target,
              threshold,
              multidim_average,
              ignore_index,
              validate_args,
          )
      if task == ClassificationTask.MULTICLASS:
          if not isinstance(num_classes, int):
              raise ValueError(
                  f"Optional arg `num_classes` must be type `int` when task is {task}. Got {type(num_classes)}"
              )
          if not isinstance(top_k, int):
              raise ValueError(
                  f"Optional arg `top_k` must be type `int` when task is {task}. Got {type(top_k)}"
              )
          return multiclass_accuracy(
              preds,
              target,
              num_classes,
              average,
              top_k,
              multidim_average,
              ignore_index,
              validate_args,
          )
      if task == ClassificationTask.MULTILABEL:
          if not isinstance(num_labels, int):
              raise ValueError(
                  f"Optional arg `num_labels` must be type `int` when task is {task}. Got {type(num_labels)}"
              )
          return multilabel_accuracy(
              preds,
              target,
              num_labels,
              threshold,
              average,
              multidim_average,
              ignore_index,
              validate_args,
          )
      raise ValueError(f"Not handled value: {task}")
  
file_path: torchmetrics/src/torchmetrics/functional/classification/accuracy.py
imports: from typing import Optional
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.stat_scores import _binary_stat_scores_arg_validation, _binary_stat_scores_format, _binary_stat_scores_tensor_validation, _binary_stat_scores_update, _multiclass_stat_scores_arg_validation, _multiclass_stat_scores_format, _multiclass_stat_scores_tensor_validation, _multiclass_stat_scores_update, _multilabel_stat_scores_arg_validation, _multilabel_stat_scores_format, _multilabel_stat_scores_tensor_validation, _multilabel_stat_scores_update
  from torchmetrics.utilities.compute import _adjust_weights_safe_divide, _safe_divide
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 5 for calling a global function with example usage: accuracy(preds, target, task, threshold, num_classes, num_labels, average, multidim_average, top_k, ignore_index, validate_args) and returns: binary_accuracy, multiclass_accuracy, multilabel_accuracy