class_docstr: Compute extended edit distance score (`ExtendedEditDistance`_) for strings or list of strings.
  
  The metric utilises the Levenshtein distance and extends it by adding a jump operation.
  
  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
  
  Args:
      language: Language used in sentences. Only supports English (en) and Japanese (ja) for now.
      return_sentence_level_score: An indication of whether sentence-level EED score is to be returned
      alpha: optimal jump penalty, penalty for jumps between characters
      rho: coverage cost, penalty for repetition of characters
      deletion: penalty for deletion of character
      insertion: penalty for insertion or substitution of character
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> from torchmetrics.text import ExtendedEditDistance
      >>> preds = ["this is the prediction", "here is an other sample"]
      >>> target = ["this is the reference", "here is another one"]
      >>> eed = ExtendedEditDistance()
      >>> eed(preds=preds, target=target)
      tensor(0.3078)
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code: def __init__(
      self,
      language: Literal["en", "ja"] = "en",
      return_sentence_level_score: bool = False,
      alpha: float = 2.0,
      rho: float = 0.3,
      deletion: float = 0.2,
      insertion: float = 1.0,
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
  
      if language not in ("en", "ja"):
          raise ValueError(
              f"Expected argument `language` to either be `en` or `ja` but got {language}"
          )
      self.language: Literal["en", "ja"] = language
      self.return_sentence_level_score = return_sentence_level_score
  
      # input validation for parameters
      for param_name, param in zip(
          ["alpha", "rho", "deletion", "insertion"],
          [alpha, rho, deletion, insertion],
      ):
          if (
              not isinstance(param, float)
              or isinstance(param, float)
              and param < 0
          ):
              raise ValueError(
                  f"Parameter `{param_name}` is expected to be a non-negative float."
              )
  
      self.alpha = alpha
      self.rho = rho
      self.deletion = deletion
      self.insertion = insertion
  
      self.add_state("sentence_eed", [], dist_reduce_fx="cat")
  
file_path: torchmetrics/src/torchmetrics/text/eed.py
imports: from typing import Any, List, Optional, Sequence, Tuple, Union
  from torch import Tensor, stack
  from typing_extensions import Literal
  from torchmetrics.functional.text.eed import _eed_compute, _eed_update
  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 ExtendedEditDistance object from inherited base classes: Metric with example: obj = ExtendedEditDistance(language, return_sentence_level_score, alpha, rho, deletion, insertion)