class_docstr: Wrapper for deprecated import.
  
  >>> import torch
  >>> from torchmetrics.functional 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)
class_docstr_tok: ["Wrapper","for","deprecated","import",".",">",">",">","import","torch",">",">",">","from","torchmetrics.functional","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",")"]
code: def __init__(
      self,
      metric_func: Callable,
      mode: Literal[
          "speaker-wise", "permutation-wise"
      ] = "speaker-wise",
      eval_func: Literal["max", "min"] = "max",
      **kwargs: Any,
  ) -> None:
      _deprecated_root_import_class(
          "PermutationInvariantTraining", "audio"
      )
      super().__init__(
          metric_func=metric_func,
          mode=mode,
          eval_func=eval_func,
          **kwargs,
      )
  
imports: from typing import Any, Callable, Optional
  from typing_extensions import Literal
  from torchmetrics.audio.pit import PermutationInvariantTraining
  from torchmetrics.audio.sdr import ScaleInvariantSignalDistortionRatio, SignalDistortionRatio
  from torchmetrics.audio.snr import ScaleInvariantSignalNoiseRatio, SignalNoiseRatio
  from torchmetrics.utilities.prints import _deprecated_root_import_class
label_desc: Use image node_id 1 to create a new _PermutationInvariantTraining object from inherited base classes: PermutationInvariantTraining with example: obj = _PermutationInvariantTraining(metric_func, mode, eval_func)