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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docstr: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting a single value
      >>> import torch
      >>> from torchmetrics.audio import PermutationInvariantTraining
      >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
      >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
      >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
      >>> metric = PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
      ...     mode="speaker-wise", eval_func="max")
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.audio import PermutationInvariantTraining
      >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
      >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
      >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
      >>> metric = PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
      ...     mode="speaker-wise", eval_func="max")
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(preds, target))
      >>> fig_, ax_ = metric.plot(values)
docstr_tok: ["Plot","a","single","or","multiple","values","from","the","metric",".","Args",":","val",":","Either","a","single","result","from","calling","`","metric.forward","`","or","`","metric.compute","`","or","a","list","of","these","results",".","If","no","value","is","provided",",","will","automatically","call","`","metric.compute","`","and","plot","that","result",".","ax",":","An","matplotlib","axis","object",".","If","provided","will","add","plot","to","that","axis","Returns",":","Figure","and","Axes","object","Raises",":","ModuleNotFoundError",":","If","`","matplotlib","`","is","not","installed","..","plot",":",":",":","scale",":","75",">",">",">","#","Example","plotting","a","single","value",">",">",">","import","torch",">",">",">","from","torchmetrics.audio","import","PermutationInvariantTraining",">",">",">","from","torchmetrics.functional.audio","import","scale_invariant_signal_noise_ratio",">",">",">","preds","=","torch.randn","(","3",",","2",",","5",")","#","[","batch",",","spk",",","time","]",">",">",">","target","=","torch.randn","(","3",",","2",",","5",")","#","[","batch",",","spk",",","time","]",">",">",">","metric","=","PermutationInvariantTraining","(","scale_invariant_signal_noise_ratio",",","...","mode=","''","speaker-wise","''",",","eval_func=","''","max","''",")",">",">",">","metric.update","(","preds",",","target",")",">",">",">","fig_",",","ax_","=","metric.plot","(",")","..","plot",":",":",":","scale",":","75",">",">",">","#","Example","plotting","multiple","values",">",">",">","import","torch",">",">",">","from","torchmetrics.audio","import","PermutationInvariantTraining",">",">",">","from","torchmetrics.functional.audio","import","scale_invariant_signal_noise_ratio",">",">",">","preds","=","torch.randn","(","3",",","2",",","5",")","#","[","batch",",","spk",",","time","]",">",">",">","target","=","torch.randn","(","3",",","2",",","5",")","#","[","batch",",","spk",",","time","]",">",">",">","metric","=","PermutationInvariantTraining","(","scale_invariant_signal_noise_ratio",",","...","mode=","''","speaker-wise","''",",","eval_func=","''","max","''",")",">",">",">","values","=","[","]",">",">",">","for","_","in","range","(","10",")",":","...","values.append","(","metric","(","preds",",","target",")",")",">",">",">","fig_",",","ax_","=","metric.plot","(","values",")"]
code: def plot(
      self,
      val: Union[Tensor, Sequence[Tensor], None] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting a single value
          >>> import torch
          >>> from torchmetrics.audio import PermutationInvariantTraining
          >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
          >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
          >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
          >>> metric = PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
          ...     mode="speaker-wise", eval_func="max")
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.audio import PermutationInvariantTraining
          >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
          >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
          >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
          >>> metric = PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
          ...     mode="speaker-wise", eval_func="max")
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(preds, target))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
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 4 for calling the PermutationInvariantTraining obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self