class_docstr: Computes Root Mean Squared Error (RMSE) using sliding window.
  
  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
  
  - ``rmse_sw`` (:class:`~torch.Tensor`): returns float scalar tensor with average RMSE-SW value over sample
  
  Args:
      window_size: Sliding window used for rmse calculation
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> from torchmetrics.image import RootMeanSquaredErrorUsingSlidingWindow
      >>> g = torch.manual_seed(22)
      >>> preds = torch.rand(4, 3, 16, 16)
      >>> target = torch.rand(4, 3, 16, 16)
      >>> rmse_sw = RootMeanSquaredErrorUsingSlidingWindow()
      >>> rmse_sw(preds, target)
      tensor(0.3999)
  
  Raises:
      ValueError: If ``window_size`` is not a positive integer.
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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 RootMeanSquaredErrorUsingSlidingWindow
      >>> metric = RootMeanSquaredErrorUsingSlidingWindow()
      >>> metric.update(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.image import RootMeanSquaredErrorUsingSlidingWindow
      >>> metric = RootMeanSquaredErrorUsingSlidingWindow()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16)))
      >>> 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 RootMeanSquaredErrorUsingSlidingWindow
          >>> metric = RootMeanSquaredErrorUsingSlidingWindow()
          >>> metric.update(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.image import RootMeanSquaredErrorUsingSlidingWindow
          >>> metric = RootMeanSquaredErrorUsingSlidingWindow()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16)))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/image/rmse_sw.py
imports: from typing import Any, Dict, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torchmetrics.functional.image.rmse_sw import _rmse_sw_compute, _rmse_sw_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 RootMeanSquaredErrorUsingSlidingWindow obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self