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: Update current score with batch of input images.
docstr_tok: ["Update","current","score","with","batch","of","input","images","."]
code: def update(self, img: Tensor) -> None:
      """Update current score with batch of input images."""
      score, num_elements = _total_variation_update(img)
      if self.reduction is None or self.reduction == "none":
          self.score_list.append(score)
      else:
          self.score += score.sum()
      self.num_elements += num_elements
  
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 2 for calling the TotalVariation obj's underlying member method code with example usage: obj.update(img) without return types