class_docstr: Compute `MultiScaleSSIM`_, Multi-scale Structural Similarity Index Measure.
  
  This metric is is a generalization of Structural Similarity Index Measure by incorporating image details at
  different resolution scores.
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): Predictions from model
  - ``target`` (:class:`~torch.Tensor`): Ground truth values
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``msssim`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average MSSSIM
    value over sample else returns tensor of shape ``(N,)`` with SSIM values per sample
  
  Args:
      gaussian_kernel: If ``True`` (default), a gaussian kernel is used, if false a uniform kernel is used
      kernel_size: size of the gaussian kernel
      sigma: Standard deviation of the gaussian kernel
      reduction: a method to reduce metric score over labels.
  
          - ``'elementwise_mean'``: takes the mean
          - ``'sum'``: takes the sum
          - ``'none'`` or ``None``: no reduction will be applied
  
      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.
      k1: Parameter of structural similarity index measure.
      k2: Parameter of structural similarity index measure.
      betas: Exponent parameters for individual similarities and contrastive sensitivities returned by different image
          resolutions.
      normalize: When MultiScaleStructuralSimilarityIndexMeasure loss is used for training, it is desirable to use
          normalizes to improve the training stability. This `normalize` argument is out of scope of the original
          implementation [1], and it is adapted from https://github.com/jorge-pessoa/pytorch-msssim instead.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Return:
      Tensor with Multi-Scale SSIM score
  
  Raises:
      ValueError:
          If ``kernel_size`` is not an int or a Sequence of ints with size 2 or 3.
      ValueError:
          If ``betas`` is not a tuple of floats with length 2.
      ValueError:
          If ``normalize`` is neither `None`, `ReLU` nor `simple`.
  
  Example:
      >>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
      >>> import torch
      >>> gen = torch.manual_seed(42)
      >>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
      >>> target = preds * 0.75
      >>> ms_ssim = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
      >>> ms_ssim(preds, target)
      tensor(0.9627)
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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
      >>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
      >>> import torch
      >>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
      >>> target = preds * 0.75
      >>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
      >>> import torch
      >>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
      >>> target = preds * 0.75
      >>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
      >>> 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
          >>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
          >>> import torch
          >>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
          >>> target = preds * 0.75
          >>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
          >>> import torch
          >>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
          >>> target = preds * 0.75
          >>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(preds, target))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
imports: from typing import Any, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.image.ssim import _multiscale_ssim_update, _ssim_check_inputs, _ssim_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 4 for calling the MultiScaleStructuralSimilarityIndexMeasure obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self