class_docstr: Calculates recall for binary, multiclass and multilabel data.
  
  .. math:: \text{Recall} = \frac{ TP }{ TP + FN }
  
  where :math:`\text{TP}` is true positives and :math:`\text{FN}` is false negatives.
  
  - ``update`` must receive output of the form ``(y_pred, y)``.
  - `y_pred` must be in the following shape (batch_size, num_categories, ...) or (batch_size, ...).
  - `y` must be in the following shape (batch_size, ...).
  
  Args:
      output_transform: a callable that is used to transform the
          :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
          form expected by the metric. This can be useful if, for example, you have a multi-output model and
          you want to compute the metric with respect to one of the outputs.
      average: available options are
  
          False
            default option. For multicalss and multilabel inputs, per class and per label
            metric is returned respectively.
  
          None
            like `False` option except that per class metric is returned for binary data as well.
            For compatibility with Scikit-Learn api.
  
          'micro'
            Metric is computed counting stats of classes/labels altogether.
  
            .. math::
                \text{Micro Recall} = \frac{\sum_{k=1}^C TP_k}{\sum_{k=1}^C TP_k+FN_k}
  
            where :math:`C` is the number of classes/labels (2 in binary case). :math:`k` in
            :math:`TP_k` and :math:`FN_k`means that the measures are computed for class/label :math:`k` (in
            a one-vs-rest sense in multiclass case).
  
            For binary and multiclass inputs, this is equivalent with accuracy,
            so use :class:`~ignite.metrics.accuracy.Accuracy`.
  
          'samples'
            for multilabel input, at first, recall is computed on a
            per sample basis and then average across samples is returned.
  
            .. math::
                \text{Sample-averaged Recall} = \frac{\sum_{n=1}^N \frac{TP_n}{TP_n+FN_n}}{N}
  
            where :math:`N` is the number of samples. :math:`n` in :math:`TP_n` and :math:`FN_n`
            means that the measures are computed for sample :math:`n`, across labels.
  
            Incompatible with binary and multiclass inputs.
  
          'weighted'
            like macro recall but considers class/label imbalance. For binary and multiclass
            input, it computes metric for each class then returns average of them weighted by
            support of classes (number of actual samples in each class). For multilabel input,
            it computes recall for each label then returns average of them weighted by support
            of labels (number of actual positive samples in each label).
  
            .. math::
                Recall_k = \frac{TP_k}{TP_k+FN_k}
  
            .. math::
                \text{Weighted Recall} = \frac{\sum_{k=1}^C P_k * Recall_k}{N}
  
            where :math:`C` is the number of classes (2 in binary case). :math:`P_k` is the number
            of samples belonged to class :math:`k` in binary and multiclass case, and the number of
            positive samples belonged to label :math:`k` in multilabel case.
  
            Note that for binary and multiclass data, weighted recall is equivalent
            with accuracy, so use :class:`~ignite.metrics.accuracy.Accuracy`.
  
          macro
            computes macro recall which is unweighted average of metric computed across
            classes or labels.
  
            .. math::
                \text{Macro Recall} = \frac{\sum_{k=1}^C Recall_k}{C}
  
            where :math:`C` is the number of classes (2 in binary case).
  
          True
            like macro option. For backward compatibility.
      is_multilabel: flag to use in multilabel case. By default, value is False.
      device: specifies which device updates are accumulated on. Setting the metric's
          device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By
          default, CPU.
  
  Examples:
  
      For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
  
      .. include:: defaults.rst
          :start-after: :orphan:
  
      Binary case. In binary and multilabel cases, the elements of
      `y` and `y_pred` should have 0 or 1 values.
  
      .. testcode:: 1
  
          metric = Recall()
          two_class_metric = Recall(average=None) # Returns recall for both classes
          metric.attach(default_evaluator, "recall")
          two_class_metric.attach(default_evaluator, "both classes recall")
          y_true = torch.tensor([1, 0, 1, 1, 0, 1])
          y_pred = torch.tensor([1, 0, 1, 0, 1, 1])
          state = default_evaluator.run([[y_pred, y_true]])
          print(f"Recall: {state.metrics['recall']}")
          print(f"Recall for class 0 and class 1: {state.metrics['both classes recall']}")
  
      .. testoutput:: 1
  
          Recall: 0.75
          Recall for class 0 and class 1: tensor([0.5000, 0.7500], dtype=torch.float64)
  
      Multiclass case
  
      .. testcode:: 2
  
          metric = Recall()
          macro_metric = Recall(average=True)
  
          metric.attach(default_evaluator, "recall")
          macro_metric.attach(default_evaluator, "macro recall")
  
          y_true = torch.tensor([2, 0, 2, 1, 0])
          y_pred = torch.tensor([
              [0.0266, 0.1719, 0.3055],
              [0.6886, 0.3978, 0.8176],
              [0.9230, 0.0197, 0.8395],
              [0.1785, 0.2670, 0.6084],
              [0.8448, 0.7177, 0.7288]
          ])
          state = default_evaluator.run([[y_pred, y_true]])
          print(f"Recall: {state.metrics['recall']}")
          print(f"Macro Recall: {state.metrics['macro recall']}")
  
      .. testoutput:: 2
  
          Recall: tensor([0.5000, 0.0000, 0.5000], dtype=torch.float64)
          Macro Recall: 0.3333333333333333
  
      Multilabel case, the shapes must be (batch_size, num_categories, ...)
  
      .. testcode:: 3
  
          metric = Recall(is_multilabel=True)
          micro_metric = Recall(is_multilabel=True, average='micro')
          macro_metric = Recall(is_multilabel=True, average=True)
          samples_metric = Recall(is_multilabel=True, average='samples')
  
          metric.attach(default_evaluator, "recall")
          micro_metric.attach(default_evaluator, "micro recall")
          macro_metric.attach(default_evaluator, "macro recall")
          samples_metric.attach(default_evaluator, "samples recall")
  
          y_true = torch.tensor([
              [0, 0, 1],
              [0, 0, 0],
              [0, 0, 0],
              [1, 0, 0],
              [0, 1, 1],
          ])
          y_pred = torch.tensor([
              [1, 1, 0],
              [1, 0, 1],
              [1, 0, 0],
              [1, 0, 1],
              [1, 1, 0],
          ])
          state = default_evaluator.run([[y_pred, y_true]])
          print(f"Recall: {state.metrics['recall']}")
          print(f"Micro Recall: {state.metrics['micro recall']}")
          print(f"Macro Recall: {state.metrics['macro recall']}")
          print(f"Samples Recall: {state.metrics['samples recall']}")
  
      .. testoutput:: 3
  
          Recall: tensor([1., 1., 0.], dtype=torch.float64)
          Micro Recall: 0.5
          Macro Recall: 0.6666666666666666
          Samples Recall: 0.3
  
      Thresholding of predictions can be done as below:
  
      .. testcode:: 4
  
          def thresholded_output_transform(output):
              y_pred, y = output
              y_pred = torch.round(y_pred)
              return y_pred, y
  
          metric = Recall(output_transform=thresholded_output_transform)
          metric.attach(default_evaluator, "recall")
          y_true = torch.tensor([1, 0, 1, 1, 0, 1])
          y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65])
          state = default_evaluator.run([[y_pred, y_true]])
          print(state.metrics['recall'])
  
      .. testoutput:: 4
  
          0.75
  
  .. versionchanged:: 0.4.10
          Some new options were added to `average` parameter.
code: def update(self, output: Sequence[torch.Tensor]) -> None:
      self._check_shape(output)
      self._check_type(output)
      _, y, correct = self._prepare_output(output)
  
      if self._average == "samples":
          actual_positives = y.sum(dim=1)
          true_positives = correct.sum(dim=1)
          self._numerator += torch.sum(
              true_positives / (actual_positives + self.eps)
          )
          self._denominator += y.size(0)
      elif self._average == "micro":
          self._denominator += y.sum()
          self._numerator += correct.sum()
      else:  # _average in [False, 'macro', 'weighted']
          self._denominator += y.sum(dim=0)
          self._numerator += correct.sum(dim=0)
  
          if self._average == "weighted":
              self._weight += y.sum(dim=0)
  
      self._updated = True
  
imports: from typing import Sequence
  import torch
  from ignite.metrics.metric import reinit__is_reduced
  from ignite.metrics.precision import _BasePrecisionRecall
label_desc: Use image node_id 1 for calling the Recall obj's underlying member method code with example usage: obj.update(output) without return types