code: def _mean_squared_log_error_update(
      preds: Tensor, target: Tensor
  ) -> Tuple[Tensor, int]:
      """Return variables required to compute Mean Squared Log Error. Checks for same shape of tensors.
  
      Args:
          preds: Predicted tensor
          target: Ground truth tensor
  
      """
      _check_same_shape(preds, target)
      sum_squared_log_error = torch.sum(
          torch.pow(torch.log1p(preds) - torch.log1p(target), 2)
      )
      return sum_squared_log_error, target.numel()
  
imports: from typing import Tuple, Union
  import torch
  from torch import Tensor
  from torchmetrics.utilities.checks import _check_same_shape
label_desc: Use image node_id 1 for calling a global function with example usage: _mean_squared_log_error_update(preds, target) and returns: sum_squared_log_error,  target