class_docstr: Compute `Minkowski Distance`_.
  
  .. math::
      d_{\text{Minkowski}} = \sum_{i}^N (| y_i - \hat{y_i} |^p)^\frac{1}{p}
  
  where
      :math: `y` is a tensor of target values,
      :math: `\hat{y}` is a tensor of predictions,
      :math: `\p` is a non-negative integer or floating-point number
  
  This metric can be seen as generalized version of the standard euclidean distance which corresponds to minkowski
  distance with p=2.
  
  Args:
      p: int or float larger than 1, exponent to which the difference between preds and target is to be raised
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> from torchmetrics.regression import MinkowskiDistance
      >>> target = tensor([1.0, 2.8, 3.5, 4.5])
      >>> preds = tensor([6.1, 2.11, 3.1, 5.6])
      >>> minkowski_distance = MinkowskiDistance(3)
      >>> minkowski_distance(preds, target)
      tensor(5.1220)
class_docstr_tok: ["Compute","`","Minkowski","Distance","`","_",".","..","math",":",":","d_","{","\\text","{","Minkowski","}","}","=","\\sum_","{","i","}","^N","(","|","y_i","-","\\hat","{","y_i","}","|^p",")","^\\frac","{","1","}","{","p","}","where",":","math",":","`","y","`","is","a","tensor","of","target","values",",",":","math",":","`","\\hat","{","y","}","`","is","a","tensor","of","predictions",",",":","math",":","`","\\p","`","is","a","non-negative","integer","or","floating-point","number","This","metric","can","be","seen","as","generalized","version","of","the","standard","euclidean","distance","which","corresponds","to","minkowski","distance","with","p=2",".","Args",":","p",":","int","or","float","larger","than","1",",","exponent","to","which","the","difference","between","preds","and","target","is","to","be","raised","kwargs",":","Additional","keyword","arguments",",","see",":","ref",":","`","Metric","kwargs","`","for","more","info",".","Example",":",">",">",">","from","torchmetrics.regression","import","MinkowskiDistance",">",">",">","target","=","tensor","(","[","1.0",",","2.8",",","3.5",",","4.5","]",")",">",">",">","preds","=","tensor","(","[","6.1",",","2.11",",","3.1",",","5.6","]",")",">",">",">","minkowski_distance","=","MinkowskiDistance","(","3",")",">",">",">","minkowski_distance","(","preds",",","target",")","tensor","(","5.1220",")"]
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
  
      >>> from torch import randn
      >>> # Example plotting a single value
      >>> from torchmetrics.regression import MinkowskiDistance
      >>> metric = MinkowskiDistance(p=3)
      >>> metric.update(randn(10,), randn(10,))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> from torch import randn
      >>> # Example plotting multiple values
      >>> from torchmetrics.regression import MinkowskiDistance
      >>> metric = MinkowskiDistance(p=3)
      >>> values = []
      >>> for _ in range(10):
      ...     values.append(metric(randn(10,), randn(10,)))
      >>> fig, ax = metric.plot(values)
docstr_tok: ["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",">",">",">","from","torch","import","randn",">",">",">","#","Example","plotting","a","single","value",">",">",">","from","torchmetrics.regression","import","MinkowskiDistance",">",">",">","metric","=","MinkowskiDistance","(","p=3",")",">",">",">","metric.update","(","randn","(","10",",",")",",","randn","(","10",",",")",")",">",">",">","fig_",",","ax_","=","metric.plot","(",")","..","plot",":",":",":","scale",":","75",">",">",">","from","torch","import","randn",">",">",">","#","Example","plotting","multiple","values",">",">",">","from","torchmetrics.regression","import","MinkowskiDistance",">",">",">","metric","=","MinkowskiDistance","(","p=3",")",">",">",">","values","=","[","]",">",">",">","for","_","in","range","(","10",")",":","...","values.append","(","metric","(","randn","(","10",",",")",",","randn","(","10",",",")",")",")",">",">",">","fig",",","ax","=","metric.plot","(","values",")"]
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
  
          >>> from torch import randn
          >>> # Example plotting a single value
          >>> from torchmetrics.regression import MinkowskiDistance
          >>> metric = MinkowskiDistance(p=3)
          >>> metric.update(randn(10,), randn(10,))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> from torch import randn
          >>> # Example plotting multiple values
          >>> from torchmetrics.regression import MinkowskiDistance
          >>> metric = MinkowskiDistance(p=3)
          >>> values = []
          >>> for _ in range(10):
          ...     values.append(metric(randn(10,), randn(10,)))
          >>> fig, ax = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/regression/minkowski.py
imports: from typing import Any, Optional, Sequence, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.regression.minkowski import _minkowski_distance_compute, _minkowski_distance_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.exceptions import TorchMetricsUserError
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 4 for calling the MinkowskiDistance obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self