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.)
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code: def __init__(
      self,
      ratio: float = 4,
      reduction: Literal[
          "elementwise_mean", "sum", "none", None
      ] = "elementwise_mean",
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
      rank_zero_warn(
          "Metric `UniversalImageQualityIndex` will save all targets and"
          " predictions in buffer. For large datasets this may lead"
          " to large memory footprint."
      )
  
      self.add_state("preds", default=[], dist_reduce_fx="cat")
      self.add_state("target", default=[], dist_reduce_fx="cat")
      self.ratio = ratio
      self.reduction = reduction
  
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 1 to create a new ErrorRelativeGlobalDimensionlessSynthesis object from inherited base classes: Metric with example: obj = ErrorRelativeGlobalDimensionlessSynthesis(ratio, reduction)