class_docstr: Compute Total Variation loss (`TV`_).
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``img`` (:class:`~torch.Tensor`): A tensor of shape ``(N, C, H, W)`` consisting of images
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``sdi`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average TV value
    over sample else returns tensor of shape ``(N,)`` with TV values per sample
  
  Args:
      reduction: a method to reduce metric score over samples
  
          - ``'mean'``: takes the mean over samples
          - ``'sum'``: takes the sum over samples
          - ``None`` or ``'none'``: return the score per sample
  
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Raises:
      ValueError:
          If ``reduction`` is not one of ``'sum'``, ``'mean'``, ``'none'`` or ``None``
  
  Example:
      >>> import torch
      >>> from torchmetrics.image import TotalVariation
      >>> _ = torch.manual_seed(42)
      >>> tv = TotalVariation()
      >>> img = torch.rand(5, 3, 28, 28)
      >>> tv(img)
      tensor(7546.8018)
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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.image import TotalVariation
      >>> metric = TotalVariation()
      >>> metric.update(torch.rand(5, 3, 28, 28))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.image import TotalVariation
      >>> metric = TotalVariation()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(5, 3, 28, 28)))
      >>> 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.image import TotalVariation
          >>> metric = TotalVariation()
          >>> metric.update(torch.rand(5, 3, 28, 28))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.image import TotalVariation
          >>> metric = TotalVariation()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(5, 3, 28, 28)))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/image/tv.py
imports: from typing import Any, List, Optional, Sequence, Union
  import torch
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics.functional.image.tv import _total_variation_compute, _total_variation_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  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 TotalVariation obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self