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: 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
      >>> import torch
      >>> from torchmetrics.wrappers import MinMaxMetric
      >>> from torchmetrics.classification import BinaryAccuracy
      >>> metric = MinMaxMetric(BinaryAccuracy())
      >>> metric.update(torch.randint(2, (20,)), torch.randint(2, (20,)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.wrappers import MinMaxMetric
      >>> from torchmetrics.classification import BinaryAccuracy
      >>> metric = MinMaxMetric(BinaryAccuracy())
      >>> values = [ ]
      >>> for _ in range(3):
      ...     values.append(metric(torch.randint(2, (20,)), torch.randint(2, (20,))))
      >>> 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
          >>> import torch
          >>> from torchmetrics.wrappers import MinMaxMetric
          >>> from torchmetrics.classification import BinaryAccuracy
          >>> metric = MinMaxMetric(BinaryAccuracy())
          >>> metric.update(torch.randint(2, (20,)), torch.randint(2, (20,)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.wrappers import MinMaxMetric
          >>> from torchmetrics.classification import BinaryAccuracy
          >>> metric = MinMaxMetric(BinaryAccuracy())
          >>> values = [ ]
          >>> for _ in range(3):
          ...     values.append(metric(torch.randint(2, (20,)), torch.randint(2, (20,))))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
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 7 for calling the MinMaxMetric obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self