class_docstr: Wrapper metric that tracks both the minimum and maximum of a scalar/tensor across an experiment.
  
  The min/max value will be updated each time ``.compute`` is called.
  
  Args:
      base_metric:
          The metric of which you want to keep track of its maximum and minimum values.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Raises:
      ValueError
          If ``base_metric` argument is not a subclasses instance of ``torchmetrics.Metric``
  
  Example::
      >>> import torch
      >>> from torchmetrics.wrappers import MinMaxMetric
      >>> from torchmetrics.classification import BinaryAccuracy
      >>> from pprint import pprint
      >>> base_metric = BinaryAccuracy()
      >>> minmax_metric = MinMaxMetric(base_metric)
      >>> preds_1 = torch.Tensor([[0.1, 0.9], [0.2, 0.8]])
      >>> preds_2 = torch.Tensor([[0.9, 0.1], [0.2, 0.8]])
      >>> labels = torch.Tensor([[0, 1], [0, 1]]).long()
      >>> pprint(minmax_metric(preds_1, labels))
      {'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
      >>> pprint(minmax_metric.compute())
      {'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
      >>> minmax_metric.update(preds_2, labels)
      >>> pprint(minmax_metric.compute())
      {'max': tensor(1.), 'min': tensor(0.7500), 'raw': tensor(0.7500)}
docstr: Compute the underlying metric as well as max and min values for this metric.
  
  Returns a dictionary that consists of the computed value (``raw``), as well as the minimum (``min``) and maximum
  (``max``) values.
docstr_tok: ["Compute","the","underlying","metric","as","well","as","max","and","min","values","for","this","metric",".","Returns","a","dictionary","that","consists","of","the","computed","value","(","``","raw","``",")",",","as","well","as","the","minimum","(","``","min","``",")","and","maximum","(","``","max","``",")","values","."]
code: def compute(self) -> Dict[str, Tensor]:
      """Compute the underlying metric as well as max and min values for this metric.
  
      Returns a dictionary that consists of the computed value (``raw``), as well as the minimum (``min``) and maximum
      (``max``) values.
  
      """
      val = self._base_metric.compute()
      if not self._is_suitable_val(val):
          raise RuntimeError(
              f"Returned value from base metric should be a float or scalar tensor, but got {val}."
          )
      self.max_val = (
          val
          if self.max_val.to(val.device) < val
          else self.max_val.to(val.device)
      )
      self.min_val = (
          val
          if self.min_val.to(val.device) > val
          else self.min_val.to(val.device)
      )
      return {"raw": val, "max": self.max_val, "min": self.min_val}
  
file_path: torchmetrics/src/torchmetrics/wrappers/minmax.py
imports: from typing import Any, Dict, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
  from torchmetrics.wrappers.abstract import WrapperMetric
label_desc: Use image node_id 3 for calling the MinMaxMetric obj's underlying member method code with example usage: obj.compute() and returns: dict