code: def mean_squared_log_error(preds: Tensor, target: Tensor) -> Tensor:
      """Compute mean squared log error.
  
      Args:
          preds: estimated labels
          target: ground truth labels
  
      Return:
          Tensor with RMSLE
  
      Example:
          >>> from torchmetrics.functional.regression import mean_squared_log_error
          >>> x = torch.tensor([0., 1, 2, 3])
          >>> y = torch.tensor([0., 1, 2, 2])
          >>> mean_squared_log_error(x, y)
          tensor(0.0207)
  
      .. note::
          Half precision is only support on GPU for this metric
  
      """
      sum_squared_log_error, num_obs = _mean_squared_log_error_update(
          preds, target
      )
      return _mean_squared_log_error_compute(
          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 3 for calling a global function with example usage: mean_squared_log_error(preds, target) and returns: _mean_squared_log_error_compute