class_docstr: Calculate `SQuAD Metric`_ which is a metric for evaluating question answering models.
  
  This metric corresponds to the scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  -  ``preds`` (:class:`~Dict`): A Dictionary or List of Dictionary-s that map ``id`` and ``prediction_text`` to
     the respective values
  
     Example ``prediction``:
  
              .. code-block:: python
  
                  {"prediction_text": "TorchMetrics is awesome", "id": "123"}
  
  
  - ``target`` (:class:`~Dict`): A Dictionary or List of Dictionary-s that contain the ``answers`` and ``id`` in
    the SQuAD Format.
  
      Example ``target``:
  
      .. code-block:: python
  
          {
              'answers': [{'answer_start': [1], 'text': ['This is a test answer']}],
              'id': '1',
          }
  
      Reference SQuAD Format:
  
      .. code-block:: python
  
          {
              'answers': {'answer_start': [1], 'text': ['This is a test text']},
              'context': 'This is a test context.',
              'id': '1',
              'question': 'Is this a test?',
              'title': 'train test'
          }
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  -  ``squad`` (:class:`~Dict`): A dictionary containing the F1 score (key: "f1"),
      and Exact match score (key: "exact_match") for the batch.
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> from torchmetrics.text import SQuAD
      >>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
      >>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
      >>> squad = SQuAD()
      >>> squad(preds, target)
      {'exact_match': tensor(100.), 'f1': tensor(100.)}
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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
      >>> from torchmetrics.text import SQuAD
      >>> metric = SQuAD()
      >>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
      >>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> from torchmetrics.text import SQuAD
      >>> metric = SQuAD()
      >>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
      >>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
      >>> 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 SQuAD
          >>> metric = SQuAD()
          >>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
          >>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> from torchmetrics.text import SQuAD
          >>> metric = SQuAD()
          >>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
          >>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
          >>> 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/squad.py
imports: from typing import Any, Dict, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torchmetrics import Metric
  from torchmetrics.functional.text.squad import PREDS_TYPE, TARGETS_TYPE, _squad_compute, _squad_input_check, _squad_update
  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 SQuAD obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self