class_docstr: Calculate `Permutation invariant training`_ (PIT).
  
  This metric can evaluate models for speaker independent multi-talker speech separation in a permutation
  invariant way.
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(batch_size,num_speakers,...)``
  - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(batch_size,num_speakers,...)``
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``pesq`` (:class:`~torch.Tensor`): float scalar tensor with average PESQ value over samples
  
  Args:
      metric_func:
          a metric function accept a batch of target and estimate.
  
          if `mode`==`'speaker-wise'`, then ``metric_func(preds[:, i, ...], target[:, j, ...])`` is called
          and expected to return a batch of metric tensors ``(batch,)``;
  
          if `mode`==`'permutation-wise'`, then ``metric_func(preds[:, p, ...], target[:, :, ...])`` is called,
          where `p` is one possible permutation, e.g. [0,1] or [1,0] for 2-speaker case, and expected to return
          a batch of metric tensors ``(batch,)``;
      mode:
          can be `'speaker-wise'` or `'permutation-wise'`.
      eval_func:
          the function to find the best permutation, can be 'min' or 'max', i.e. the smaller the better
          or the larger the better.
      kwargs: Additional keyword arguments for either the ``metric_func`` or distributed communication,
          see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> import torch
      >>> from torchmetrics.audio import PermutationInvariantTraining
      >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
      >>> _ = torch.manual_seed(42)
      >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
      >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
      >>> pit = PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
      ...     mode="speaker-wise", eval_func="max")
      >>> pit(preds, target)
      tensor(-2.1065)
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code: def __init__(
      self,
      metric_func: Callable,
      mode: Literal[
          "speaker-wise", "permutation-wise"
      ] = "speaker-wise",
      eval_func: Literal["max", "min"] = "max",
      **kwargs: Any,
  ) -> None:
      base_kwargs: Dict[str, Any] = {
          "dist_sync_on_step": kwargs.pop("dist_sync_on_step", False),
          "process_group": kwargs.pop("process_group", None),
          "dist_sync_fn": kwargs.pop("dist_sync_fn", None),
      }
      super().__init__(**base_kwargs)
      self.metric_func = metric_func
      self.mode = mode
      self.eval_func = eval_func
      self.kwargs = kwargs
  
      self.add_state(
          "sum_pit_metric", default=tensor(0.0), dist_reduce_fx="sum"
      )
      self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
  
file_path: torchmetrics/src/torchmetrics/audio/pit.py
imports: from typing import Any, Callable, Dict, Optional, Sequence, Union
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics.functional.audio.pit import permutation_invariant_training
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 1 to create a new PermutationInvariantTraining object from inherited base classes: Metric with example: obj = PermutationInvariantTraining(metric_func, mode, eval_func)