class_docstr: `Compute Peak Signal-to-Noise Ratio`_ (PSNR).
  
  .. math:: \text{PSNR}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)}\right)
  
  Where :math:`\text{MSE}` denotes the `mean-squared-error`_ function.
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)``
  - ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)``
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``psnr`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average PSNR value
    over sample else returns tensor of shape ``(N,)`` with PSNR values per sample
  
  Args:
      data_range:
          the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
          the range is calculated as the difference and input is clamped between the values.
          The ``data_range`` must be given when ``dim`` is not None.
      base: a base of a logarithm to use.
      reduction: a method to reduce metric score over labels.
  
          - ``'elementwise_mean'``: takes the mean (default)
          - ``'sum'``: takes the sum
          - ``'none'`` or ``None``: no reduction will be applied
  
      dim:
          Dimensions to reduce PSNR scores over, provided as either an integer or a list of integers. Default is
          None meaning scores will be reduced across all dimensions and all batches.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Raises:
      ValueError:
          If ``dim`` is not ``None`` and ``data_range`` is not given.
  
  Example:
      >>> from torchmetrics.image import PeakSignalNoiseRatio
      >>> psnr = PeakSignalNoiseRatio()
      >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
      >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
      >>> psnr(preds, target)
      tensor(2.5527)
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 PeakSignalNoiseRatio
      >>> metric = PeakSignalNoiseRatio()
      >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
      >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.image import PeakSignalNoiseRatio
      >>> metric = PeakSignalNoiseRatio()
      >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
      >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(preds, target))
      >>> 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 PeakSignalNoiseRatio
          >>> metric = PeakSignalNoiseRatio()
          >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
          >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.image import PeakSignalNoiseRatio
          >>> metric = PeakSignalNoiseRatio()
          >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
          >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(preds, target))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/image/psnr.py
imports: from functools import partial
  from typing import Any, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics.functional.image.psnr import _psnr_compute, _psnr_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities import rank_zero_warn
  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 PeakSignalNoiseRatio obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self