class_docstr: Calculate `Relative dimensionless global error synthesis`_ (ERGAS).
  
  This metric is used to calculate the accuracy of Pan sharpened image considering normalized average error of each
  band of the result image.
  
  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
  
  - ``ergas`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average ERGAS
    value over sample else returns tensor of shape ``(N,)`` with ERGAS values per sample
  
  Args:
      ratio: ratio of high resolution to low resolution
      reduction: a method to reduce metric score over labels.
  
          - ``'elementwise_mean'``: takes the mean (default)
          - ``'sum'``: takes the sum
          - ``'none'`` or ``None``: no reduction will be applied
  
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> import torch
      >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
      >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
      >>> target = preds * 0.75
      >>> ergas = ErrorRelativeGlobalDimensionlessSynthesis()
      >>> torch.round(ergas(preds, target))
      tensor(154.)
class_docstr_tok: ["Calculate","`","Relative","dimensionless","global","error","synthesis","`","_","(","ERGAS",")",".","This","metric","is","used","to","calculate","the","accuracy","of","Pan","sharpened","image","considering","normalized","average","error","of","each","band","of","the","result","image",".","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","-","``","ergas","``","(",":","class",":","`","~torch.Tensor","`",")",":","if","``","reduction","!","='none","'","``","returns","float","scalar","tensor","with","average","ERGAS","value","over","sample","else","returns","tensor","of","shape","``","(","N",",",")","``","with","ERGAS","values","per","sample","Args",":","ratio",":","ratio","of","high","resolution","to","low","resolution","reduction",":","a","method","to","reduce","metric","score","over","labels",".","-","``","'elementwise_mean","'","``",":","takes","the","mean","(","default",")","-","``","'sum","'","``",":","takes","the","sum","-","``","'none","'","``","or","``","None","``",":","no","reduction","will","be","applied","kwargs",":","Additional","keyword","arguments",",","see",":","ref",":","`","Metric","kwargs","`","for","more","info",".","Example",":",">",">",">","import","torch",">",">",">","from","torchmetrics.image","import","ErrorRelativeGlobalDimensionlessSynthesis",">",">",">","preds","=","torch.rand","(","[","16",",","1",",","16",",","16","]",",","generator=torch.manual_seed","(","42",")",")",">",">",">","target","=","preds","*","0.75",">",">",">","ergas","=","ErrorRelativeGlobalDimensionlessSynthesis","(",")",">",">",">","torch.round","(","ergas","(","preds",",","target",")",")","tensor","(","154",".",")"]
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 ErrorRelativeGlobalDimensionlessSynthesis
      >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
      >>> target = preds * 0.75
      >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
      >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
      >>> target = preds * 0.75
      >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
      >>> 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
          >>> import torch
          >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
          >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
          >>> target = preds * 0.75
          >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
          >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
          >>> target = preds * 0.75
          >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(preds, target))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/image/ergas.py
imports: from typing import Any, List, Optional, Sequence, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.image.ergas import _ergas_compute, _ergas_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities import rank_zero_warn
  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 ErrorRelativeGlobalDimensionlessSynthesis obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self