code: def _mean_squared_log_error_compute(
      sum_squared_log_error: Tensor, num_obs: Union[int, Tensor]
  ) -> Tensor:
      """Compute Mean Squared Log Error.
  
      Args:
          sum_squared_log_error:
              Sum of square of log errors over all observations ``(log error = log(target) - log(prediction))``
          num_obs: Number of predictions or observations
  
      Example:
          >>> preds = torch.tensor([0., 1, 2, 3])
          >>> target = torch.tensor([0., 1, 2, 2])
          >>> sum_squared_log_error, num_obs = _mean_squared_log_error_update(preds, target)
          >>> _mean_squared_log_error_compute(sum_squared_log_error, num_obs)
          tensor(0.0207)
  
      """
      return sum_squared_log_error / num_obs
  
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 2 for calling a global function with example usage: _mean_squared_log_error_compute(sum_squared_log_error, num_obs) and returns: unknown