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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code: def __init__(
      self,
      block_size: int = 8,
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
      if not isinstance(block_size, int) and block_size < 1:
          raise ValueError(
              "Argument ``block_size`` should be a positive integer"
          )
      self.block_size = block_size
  
      self.add_state(
          "sum_squared_error", default=tensor(0.0), dist_reduce_fx="sum"
      )
      self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
      self.add_state("bef", default=tensor(0.0), dist_reduce_fx="sum")
      self.add_state(
          "data_range", default=tensor(0), dist_reduce_fx="max"
      )
  
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 1 to create a new PeakSignalNoiseRatioWithBlockedEffect object from inherited base classes: Metric with example: obj = PeakSignalNoiseRatioWithBlockedEffect(block_size)