class_docstr: Calculate `Matthews correlation coefficient`_ .
  
  This metric measures the general correlation or quality of a classification.
  
  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.BinaryMatthewsCorrCoef`,
  :class:`~torchmetrics.classification.MulticlassMatthewsCorrCoef` and
  :class:`~torchmetrics.classification.MultilabelMatthewsCorrCoef` for the specific details of each argument influence
  and examples.
  
  Legacy Example:
      >>> from torch import tensor
      >>> target = tensor([1, 1, 0, 0])
      >>> preds = tensor([0, 1, 0, 0])
      >>> matthews_corrcoef = MatthewsCorrCoef(task='binary')
      >>> matthews_corrcoef(preds, target)
      tensor(0.5774)
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docstr: Initialize task metric.
docstr_tok: ["Initialize","task","metric","."]
code: def __new__(  # type: ignore[misc]
      cls: Type["MatthewsCorrCoef"],
      task: Literal["binary", "multiclass", "multilabel"],
      threshold: float = 0.5,
      num_classes: Optional[int] = None,
      num_labels: Optional[int] = None,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
      **kwargs: Any,
  ) -> Metric:
      """Initialize task metric."""
      task = ClassificationTask.from_str(task)
      kwargs.update(
          {"ignore_index": ignore_index, "validate_args": validate_args}
      )
      if task == ClassificationTask.BINARY:
          return BinaryMatthewsCorrCoef(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.`"
              )
          return MulticlassMatthewsCorrCoef(num_classes, **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 MultilabelMatthewsCorrCoef(
              num_labels, threshold, **kwargs
          )
      raise ValueError(f"Not handled value: {task}")
  
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.confusion_matrix import BinaryConfusionMatrix, MulticlassConfusionMatrix, MultilabelConfusionMatrix
  from torchmetrics.functional.classification.matthews_corrcoef import _matthews_corrcoef_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 MatthewsCorrCoef obj's underlying member method code with example usage: obj.__new__(cls, task, threshold, num_classes, num_labels, ignore_index, validate_args) and returns: BinaryMatthewsCorrCoef, MulticlassMatthewsCorrCoef, MultilabelMatthewsCorrCoef