class_docstr: Calculates the Levenshtein edit distance between two sequences.
  
  The edit distance is the number of characters that need to be substituted, inserted, or deleted, to transform the
  predicted text into the reference text. The lower the distance, the more accurate the model is considered to be.
  
  Implementation is similar to `nltk.edit_distance <https://www.nltk.org/_modules/nltk/metrics/distance.html>`_.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~Sequence`): An iterable of hypothesis corpus
  - ``target`` (:class:`~Sequence`): An iterable of iterables of reference corpus
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``eed`` (:class:`~torch.Tensor`): A tensor with the extended edit distance score. If `reduction` is set to
    ``'none'`` or ``None``, this has shape ``(N, )``, where ``N`` is the batch size. Otherwise, this is a scalar.
  
  Args:
      substitution_cost: The cost of substituting one character for another.
      reduction: a method to reduce metric score over samples.
  
          - ``'mean'``: takes the mean over samples
          - ``'sum'``: takes the sum over samples
          - ``None`` or ``'none'``: return the score per sample
  
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example::
      Basic example with two strings. Going from “rain” -> “sain” -> “shin” -> “shine” takes 3 edits:
  
      >>> from torchmetrics.text import EditDistance
      >>> metric = EditDistance()
      >>> metric(["rain"], ["shine"])
      tensor(3.)
  
  Example::
      Basic example with two strings and substitution cost of 2. Going from “rain” -> “sain” -> “shin” -> “shine”
      takes 3 edits, where two of them are substitutions:
  
      >>> from torchmetrics.text import EditDistance
      >>> metric = EditDistance(substitution_cost=2)
      >>> metric(["rain"], ["shine"])
      tensor(5.)
  
  Example::
      Multiple strings example:
  
      >>> from torchmetrics.text import EditDistance
      >>> metric = EditDistance(reduction=None)
      >>> metric(["rain", "lnaguaeg"], ["shine", "language"])
      tensor([3, 4], dtype=torch.int32)
      >>> metric = EditDistance(reduction="mean")
      >>> metric(["rain", "lnaguaeg"], ["shine", "language"])
      tensor(3.5000)
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
      >>> from torchmetrics.text import EditDistance
      >>> metric = EditDistance()
      >>> preds = ["this is the prediction", "there is an other sample"]
      >>> target = ["this is the reference", "there is another one"]
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> from torchmetrics.text import EditDistance
      >>> metric = EditDistance()
      >>> preds = ["this is the prediction", "there is an other sample"]
      >>> target = ["this is the reference", "there is another one"]
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(preds, target))
      >>> fig_, ax_ = metric.plot(values)
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code: def plot(
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = 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
          >>> from torchmetrics.text import EditDistance
          >>> metric = EditDistance()
          >>> preds = ["this is the prediction", "there is an other sample"]
          >>> target = ["this is the reference", "there is another one"]
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> from torchmetrics.text import EditDistance
          >>> metric = EditDistance()
          >>> preds = ["this is the prediction", "there is an other sample"]
          >>> target = ["this is the reference", "there is another one"]
          >>> 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/text/edit.py
imports: from typing import Any, List, Literal, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torchmetrics.functional.text.edit import _edit_distance_compute, _edit_distance_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  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 EditDistance obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self