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: Check whether min/max is a scalar value.
docstr_tok: ["Check","whether","min\/max","is","a","scalar","value","."]
code: def _is_suitable_val(val: Union[float, Tensor]) -> bool:
      """Check whether min/max is a scalar value."""
      if isinstance(val, (int, float)):
          return True
      if isinstance(val, Tensor):
          return val.numel() == 1
      return False
  
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 6 for calling the MinMaxMetric obj's underlying member method code with example usage: obj._is_suitable_val(val) and returns: False, True, unknown