code: def _ergas_update(
      preds: Tensor, target: Tensor
  ) -> Tuple[Tensor, Tensor]:
      """Update and returns variables required to compute Erreur Relative Globale Adimensionnelle de Synthèse.
  
      Args:
          preds: Predicted tensor
          target: Ground truth tensor
  
      """
      if preds.dtype != target.dtype:
          raise TypeError(
              "Expected `preds` and `target` to have the same data type."
              f" Got preds: {preds.dtype} and target: {target.dtype}."
          )
      _check_same_shape(preds, target)
      if len(preds.shape) != 4:
          raise ValueError(
              "Expected `preds` and `target` to have BxCxHxW shape."
              f" Got preds: {preds.shape} and target: {target.shape}."
          )
      return preds, target
  
file_path: torchmetrics/src/torchmetrics/functional/image/ergas.py
imports: from typing import Tuple
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.distributed import reduce
label_desc: Use image node_id 1 for calling a global function with example usage: _ergas_update(preds, target) and returns: preds,  target