code: def rmse(targets, predictions):
      r"""
      Compute the root mean squared error between two SArrays.
  
      Parameters
      ----------
      targets : SArray[float or int]
          An Sarray of ground truth target values.
  
      predictions : SArray[float or int]
          The prediction that corresponds to each target value.
          This vector must have the same length as ``targets``.
  
      Returns
      -------
      out : float
          The RMSE between the two SArrays.
  
      See Also
      --------
      max_error
  
      Notes
      -----
      The root mean squared error between two vectors, x and y, is defined as:
  
      .. math::
  
          RMSE = \sqrt{\frac{1}{N} \sum_{i=1}^N (x_i - y_i)^2}
  
      References
      ----------
      - `Wikipedia - root-mean-square deviation
        <http://en.wikipedia.org/wiki/Root-mean-square_deviation>`_
  
      Examples
      --------
      >>> targets = turicreate.SArray([3.14, 0.1, 50, -2.5])
      >>> predictions = turicreate.SArray([3.1, 0.5, 50.3, -5])
  
      >>> turicreate.evaluation.rmse(targets, predictions)
      1.2749117616525465
      """
  
      _supervised_evaluation_error_checking(targets, predictions)
      return _turicreate.extensions._supervised_streaming_evaluator(
          targets, predictions, "rmse", {}
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sarray, _check_categorical_option_type
  from turicreate.toolkits._main import ToolkitError
label_desc: Use image node_id 8 for calling a global function with example usage: rmse(targets, predictions) and returns: _turicreate