class_docstr: Computes `Peak Signal to Noise Ratio With Blocked Effect`_ (PSNRB).
  
  .. math::
      \text{PSNRB}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)-\text{B}(I, J)}\right)
  
  Where :math:`\text{MSE}` denotes the `mean-squared-error`_ function. This metric is a modified version of PSNR that
  better supports evaluation of images with blocked artifacts, that oftens occur in compressed images.
  
  .. note::
      Metric only supports grayscale images. If you have RGB images, please convert them to grayscale first.
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,1,H,W)``
  - ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,1,H,W)``
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``psnrb`` (:class:`~torch.Tensor`): float scalar tensor with aggregated PSNRB value
  
  Args:
      block_size: integer indication the block size
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> import torch
      >>> from torchmetrics.image import PeakSignalNoiseRatioWithBlockedEffect
      >>> metric = PeakSignalNoiseRatioWithBlockedEffect()
      >>> _ = torch.manual_seed(42)
      >>> preds = torch.rand(2, 1, 10, 10)
      >>> target = torch.rand(2, 1, 10, 10)
      >>> metric(preds, target)
      tensor(7.2893)
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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 PeakSignalNoiseRatioWithBlockedEffect
      >>> metric = PeakSignalNoiseRatioWithBlockedEffect()
      >>> metric.update(torch.rand(2, 1, 10, 10), torch.rand(2, 1, 10, 10))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.image import PeakSignalNoiseRatioWithBlockedEffect
      >>> metric = PeakSignalNoiseRatioWithBlockedEffect()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(2, 1, 10, 10), torch.rand(2, 1, 10, 10)))
      >>> 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 PeakSignalNoiseRatioWithBlockedEffect
          >>> metric = PeakSignalNoiseRatioWithBlockedEffect()
          >>> metric.update(torch.rand(2, 1, 10, 10), torch.rand(2, 1, 10, 10))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.image import PeakSignalNoiseRatioWithBlockedEffect
          >>> metric = PeakSignalNoiseRatioWithBlockedEffect()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(2, 1, 10, 10), torch.rand(2, 1, 10, 10)))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/image/psnrb.py
imports: from typing import Any, Optional, Sequence, Union
  import torch
  from torch import Tensor, tensor
  from torchmetrics.functional.image.psnrb import _psnrb_compute, _psnrb_update
  from torchmetrics.metric import Metric
  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 PeakSignalNoiseRatioWithBlockedEffect obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self