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: Update state with predictions and targets.
docstr_tok: ["Update","state","with","predictions","and","targets","."]
code: def update(self, preds: PREDS_TYPE, target: TARGETS_TYPE) -> None:
      """Update state with predictions and targets."""
      preds_dict, target_dict = _squad_input_check(preds, target)
      f1_score, exact_match, total = _squad_update(
          preds_dict, target_dict
      )
      self.f1_score += f1_score
      self.exact_match += exact_match
      self.total += total
  
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 2 for calling the SQuAD obj's underlying member method code with example usage: obj.update(preds, target) without return types