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: Update state with predictions and targets.
docstr_tok: ["Update","state","with","predictions","and","targets","."]
code: def update(self, preds: Tensor, target: Tensor) -> None:
      """Update state with predictions and targets."""
      if self.clamping_fn is not None:
          preds = self.clamping_fn(preds)
          target = self.clamping_fn(target)
  
      sum_squared_error, num_obs = _psnr_update(
          preds, target, dim=self.dim
      )
      if self.dim is None:
          if self.data_range is None:
              # keep track of min and max target values
              self.min_target = torch.minimum(
                  target.min(), self.min_target
              )
              self.max_target = torch.maximum(
                  target.max(), self.max_target
              )
  
          self.sum_squared_error += sum_squared_error
          self.total += num_obs
      else:
          self.sum_squared_error.append(sum_squared_error)
          self.total.append(num_obs)
  
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 2 for calling the PeakSignalNoiseRatio obj's underlying member method code with example usage: obj.update(preds, target) without return types