class_docstr: Compute the average `Hamming distance`_ (also known as Hamming loss).
  
  .. math::
      \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})
  
  Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
  and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
  tensor.
  
  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
  :class:`~torchmetrics.classification.BinaryHammingDistance`,
  :class:`~torchmetrics.classification.MulticlassHammingDistance` and
  :class:`~torchmetrics.classification.MultilabelHammingDistance` for the specific details of each argument influence
  and examples.
  
  Legacy Example:
      >>> from torch import tensor
      >>> target = tensor([[0, 1], [1, 1]])
      >>> preds = tensor([[0, 1], [0, 1]])
      >>> hamming_distance = HammingDistance(task="multilabel", num_labels=2)
      >>> hamming_distance(preds, target)
      tensor(0.2500)
docstr: Initialize task metric.
docstr_tok: ["Initialize","task","metric","."]
code: def __new__(  # type: ignore[misc]
      cls: Type["HammingDistance"],
      task: Literal["binary", "multiclass", "multilabel"],
      threshold: float = 0.5,
      num_classes: Optional[int] = None,
      num_labels: Optional[int] = None,
      average: Optional[
          Literal["micro", "macro", "weighted", "none"]
      ] = "micro",
      multidim_average: Optional[
          Literal["global", "samplewise"]
      ] = "global",
      top_k: Optional[int] = 1,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
      **kwargs: Any,
  ) -> Metric:
      """Initialize task metric."""
      task = ClassificationTask.from_str(task)
      assert (
          multidim_average is not None
      )  # noqa: S101  # needed for mypy
      kwargs.update(
          {
              "multidim_average": multidim_average,
              "ignore_index": ignore_index,
              "validate_args": validate_args,
          }
      )
      if task == ClassificationTask.BINARY:
          return BinaryHammingDistance(threshold, **kwargs)
      if task == ClassificationTask.MULTICLASS:
          if not isinstance(num_classes, int):
              raise ValueError(
                  f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`"
              )
          if not isinstance(top_k, int):
              raise ValueError(
                  f"`top_k` is expected to be `int` but `{type(top_k)} was passed.`"
              )
          return MulticlassHammingDistance(
              num_classes, top_k, average, **kwargs
          )
      if task == ClassificationTask.MULTILABEL:
          if not isinstance(num_labels, int):
              raise ValueError(
                  f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`"
              )
          return MultilabelHammingDistance(
              num_labels, threshold, average, **kwargs
          )
      raise ValueError(f"Task {task} not supported!")
  
imports: from typing import Any, Optional, Sequence, Type, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
  from torchmetrics.functional.classification.hamming import _hamming_distance_reduce
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.enums import ClassificationTask
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 1 for calling the HammingDistance obj's underlying member method code with example usage: obj.__new__(cls, task, threshold, num_classes, num_labels, average, multidim_average, top_k, ignore_index, validate_args) and returns: BinaryHammingDistance, MulticlassHammingDistance, MultilabelHammingDistance