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: 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."""
      preds, target = _ssim_check_inputs(preds, target)
      similarity = _multiscale_ssim_update(
          preds,
          target,
          self.gaussian_kernel,
          self.sigma,
          self.kernel_size,
          self.data_range,
          self.k1,
          self.k2,
          self.betas,
          self.normalize,
      )
  
      if self.reduction in ("none", None):
          self.similarity.append(similarity)
      else:
          self.similarity += similarity.sum()
  
      self.total += preds.shape[0]
  
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 2 for calling the MultiScaleStructuralSimilarityIndexMeasure obj's underlying member method code with example usage: obj.update(preds, target) without return types